{"as_of":"2026-08-16T00:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d68cdd7a310660dc0f76083f10e5a05cd94c432060ca7e952aac021d2aeccc3","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T04:48:35.281460Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1909.02651/citation-record","integrity":"/paper/1909.02651/integrity","json":"/paper/1909.02651/citation-record.json","paper":"/paper/1909.02651"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.712081Z","title":"Higher order conditional random ﬁelds in deep neural networks","venue":null,"work_id":"a6e13530-808b-4ce9-b84b-1407c3a998c6","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.582381Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:354af6f75acba06d4ad65b95a143233503e07f99c8dddb6c6e9a83af33943f20","observation_id":"7c3ce53a-3602-4f52-9f67-f59b20ae3ba8","resolution":{"observed_at":"2026-08-14T04:48:38.716094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.701583Z","title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","venue":null,"work_id":"629e754e-f64a-449c-8dff-6d0ff40aed65","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.588037Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:13fabc69974a4eada0dc91d60a09f2f7fe86f5240b0038f2474a4fe62a90b27f","observation_id":"088e77f8-dd2f-4620-820a-9ee6610685c9","resolution":{"observed_at":"2026-08-14T04:48:38.705182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.691038Z","title":"Dense decoder shortcut connections for single-pass semantic segmentation","venue":null,"work_id":"27872551-0df3-42b7-8ff8-c2756cf859d5","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.591979Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:c512149d8f925cc1db171144afff76e0d0a0551ae5fb300cc84cd1a016550c4d","observation_id":"fa8ef6fb-92f0-4e85-a409-188cfd2d9095","resolution":{"observed_at":"2026-08-14T04:48:38.695497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.680852Z","title":"Segmentation and recognition using structure from motion point clouds","venue":null,"work_id":"95534b9a-07d8-47a4-92c9-8a637b4ee953","year":2008},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.595661Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:8c44a076f999b468e932c956c0d44f5fd09744fb0c37773bdc4fbfa95c902334","observation_id":"9a0fa942-e457-4005-b735-a9a98c67685c","resolution":{"observed_at":"2026-08-14T04:48:38.685151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.671464Z","title":"Loss max-pooling for semantic image segmentation","venue":null,"work_id":"c7908c09-45d3-4860-844e-16df9578be00","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.598363Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:9c1d2b9e1b372b67cbeb13077d2809f09e3f3b024eaa3f46e1d57228a682de87","observation_id":"0c9f833a-0b2a-4c25-af71-37ce883e56b5","resolution":{"observed_at":"2026-08-14T04:48:38.674805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.499629Z","title":"Breuel, Federico Raue, and Marcus Liwicki","venue":null,"work_id":"5e602aaa-6bd6-4be4-917c-842b48776cfb","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.602610Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:377c3944002c5383d66e16dc9be5780455f0cb70aa7a62120bf448679a84c29d","observation_id":"4f8bf953-90e8-4e83-b06a-f96785b08f8e","resolution":{"observed_at":"2026-08-14T04:48:38.584096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.434423Z","title":"Coco- stuff: Thing and stuff classes in context","venue":null,"work_id":"61944f70-2bc4-4e5d-9466-07a38162cad7","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.607068Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:b6e7c12735371e08159709ec53c564b60bc620bd621fc1839ab7f33e1ed92f97","observation_id":"5da91576-b384-48c9-b4ae-5425763f10eb","resolution":{"observed_at":"2026-08-14T04:48:38.437514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.425997Z","title":"Semantic image segmentation with deep convolutional nets and fully connected crfs","venue":null,"work_id":"72de6866-6cc0-45cf-b2de-6a14d47ee013","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.653172Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:aeed209676e0b16e3467daf13419e1a7243f4b26b7b983781249ab0f7893a644","observation_id":"1b3ce2fe-7f13-465b-913e-f201941ed3c9","resolution":{"observed_at":"2026-08-14T04:48:38.429046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.417077Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","venue":null,"work_id":"4c7f030a-25f9-41b7-bc61-f1f71fadb0c1","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.780586Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:0d5171d60f2f0d0c9a2a935dab01e00072cbc8176d86e8bd9fb9680f9b31b50a","observation_id":"6692b5ed-94a5-428f-b934-aafddbb0f6e6","resolution":{"observed_at":"2026-08-14T04:48:38.420368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.408007Z","title":"Attention to scale: Scale-aware semantic image segmentation","venue":null,"work_id":"721bb066-cf29-4759-af6a-3ec9e98fd83e","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.817435Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:0a05a101ab1f1a690b10cfac8c99062b5beeab06998f94854190ea988ac7b725","observation_id":"9c7b46c4-bc12-4274-84eb-00aca40cfd24","resolution":{"observed_at":"2026-08-14T04:48:38.410940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.02611","last_updated":"2018-08-22T20:41:10Z","snapshot_observed_at":"2026-08-15T22:03:00.407650Z","submitted_at":"2018-02-07T19:37:11Z","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.02611","snapshot_observed_at":"2026-08-14T04:48:33.821635Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.821635Z"},"links":{"cited_paper":"/paper/1802.02611","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:652bf4a119db61612b7d8c9cc811f35175beb0358a4658608d6965393a198cbd","observation_id":"42ebf767-29f8-45e2-bd39-7bf0877f6130","resolution":{"observed_at":"2026-08-14T04:48:33.821635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:33.825708Z","title":"Detect what you can: Detecting and representing objects using holistic models and body parts","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.825708Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:31f7a552969b35242986e5da605664e7a57915929a0341cfe2bd52677dd01697","observation_id":"7938991e-2f53-4fc1-8beb-53b5521f77f1","resolution":{"observed_at":"2026-08-14T04:48:33.825708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.393517Z","title":"Xception: Deep learning with depthwise separable convolutions","venue":null,"work_id":"2d5b2473-e101-488e-88e9-41a98ff8a559","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.830070Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:57a0e2ed2e6e73b444f77e7964cb84ef0d86b8fba039d44ba7c6a7ef5b41faad","observation_id":"b9aa0e69-efee-4607-80c8-631f28dc0f18","resolution":{"observed_at":"2026-08-14T04:48:38.396397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:33.833713Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.833713Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:72b122650c3627dc29593c02de91fa4fe9f90cde23c3bcb92529ea29e57d5193","observation_id":"6dd3d304-3e28-4a5f-9a71-417a5f8acb42","resolution":{"observed_at":"2026-08-14T04:48:33.833713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:33.837230Z","title":"Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.837230Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:71cd570c4350504138e0a2c37070eed2cba300ed39fd331d6920c6da321f1025","observation_id":"1e01be2d-87de-4599-a7e3-4e4a41f33a4f","resolution":{"observed_at":"2026-08-14T04:48:33.837230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.374508Z","title":"Deformable convolutional networks","venue":null,"work_id":"6a740f95-d91e-495a-ac2d-cb5d2bee6f7e","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.839913Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:d65eaa06177871756133ee73758b8a5f0033a5a557ebf86832ffb591d126fe55","observation_id":"f5f1e3c3-800c-48ec-b80f-ac293fe2317e","resolution":{"observed_at":"2026-08-14T04:48:38.377964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.07674","last_updated":"2018-01-23T17:45:54Z","snapshot_observed_at":"2026-08-14T19:52:42.878007Z","submitted_at":"2018-01-23T17:45:54Z","title":"A Classification Refinement Strategy for Semantic Segmentation","version":1},"cited_work":{"arxiv_id":"1801.07674","doi":null,"metadata_source":"pith","pith_arxiv_id":"1801.07674","snapshot_observed_at":"2026-08-14T04:48:35.534135Z","title":"A Classification Refinement Strategy for Semantic Segmentation","venue":"cs.CV","work_id":"3d2d7fea-6c2a-43ce-8a8e-568c3389457d","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.843678Z"},"links":{"cited_paper":"/paper/1801.07674","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:fc607a2227f2875bbbf332eab690d2301d39984dfb4265103700b56c33d291fe","observation_id":"3b4dd7d9-41c6-4814-962b-53f94f12bcd0","resolution":{"observed_at":"2026-08-14T04:48:35.640281Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.365357Z","title":"Context contrasted feature and gated multi- scale aggregation for scene segmentation","venue":null,"work_id":"57571b1e-4a8d-491d-8818-713037ab4d12","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.848269Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:6581b049926c44c9b712abf77fe1215fde49ae976a7dd46fb0d83a92fdacc170","observation_id":"3680fe68-72cb-47ef-866e-934b7ceee333","resolution":{"observed_at":"2026-08-14T04:48:38.368895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.353876Z","title":"Learning hierarchical features for scene labeling","venue":null,"work_id":"fc1c527b-7761-4ddc-a06e-ff4500077f81","year":2013},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.851671Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:514bbf1dec23c28768cb603a9d36bad10777d522fd83124a9f9b813615317c89","observation_id":"a0db8d25-5adb-4a3e-a86e-3088d0d6c85a","resolution":{"observed_at":"2026-08-14T04:48:38.358667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.00313","last_updated":"2018-08-01T13:37:59Z","snapshot_observed_at":"2026-08-14T18:45:32.427763Z","submitted_at":"2018-08-01T13:37:59Z","title":"A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation","version":1},"cited_work":{"arxiv_id":"1808.00313","doi":null,"metadata_source":"pith","pith_arxiv_id":"1808.00313","snapshot_observed_at":"2026-08-14T04:48:35.519604Z","title":"A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation","venue":"cs.CV","work_id":"ae00129a-c74d-41fa-9a56-fc2b75f7c180","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.855167Z"},"links":{"cited_paper":"/paper/1808.00313","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:ff320a09fb79041d9f4cf107991414f3dbf61722d18d6e504b55c40a92bf882c","observation_id":"4bfebaef-7744-4a05-b093-d4caa0387604","resolution":{"observed_at":"2026-08-14T04:48:35.524376Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.227428Z","title":"Laplacian pyramid reconstruction and reﬁnement for semantic segmentation","venue":null,"work_id":"6e880bed-d4e7-4084-be6a-3ab846f271e8","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:33.950145Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:d20382dac9645d3a36c54b105fa569088687ce92b38acafeede4f3d6bbb02d54","observation_id":"52299eac-7ed4-4067-b9c6-5041d2c2e8e4","resolution":{"observed_at":"2026-08-14T04:48:38.316455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.204874Z","title":"Unpaired image captioning by language pivoting","venue":null,"work_id":"f9a25db4-1109-4ea0-a3b4-a12d9c4bd6a7","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.050544Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:595c42f671ac19a33dce1fcfabd18b17744609c10fc1b0340998800b924fd6cb","observation_id":"34434f9b-d821-42d6-b464-70ddd5ec9112","resolution":{"observed_at":"2026-08-14T04:48:38.207388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.195483Z","title":"Recent advances in convolutional neural networks","venue":null,"work_id":"637d48b8-e0a4-4ffd-9388-2752404b3444","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.095792Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:7338510564a35eda2fd74db13f8a449b1e61c007b67cc9083f839e38f3fcedee","observation_id":"2a2450eb-c7e1-42c1-901d-0ca24037addb","resolution":{"observed_at":"2026-08-14T04:48:38.198656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.099888Z","title":"Scene graph generation with external knowledge and image reconstruction","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.099888Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:677301468ed00df8dc5cedb65fc5c9e6dbce995061ea1d32c7ff757ae19f0d6b","observation_id":"c5b6f31f-4c39-4c27-9e8f-e85262d65946","resolution":{"observed_at":"2026-08-14T04:48:34.099888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.181697Z","title":"Hypercolumns for object segmentation and ﬁne-grained localization","venue":null,"work_id":"6be62a1f-8acb-44c8-98cf-6e1e95496e54","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.104626Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:45b8317ff3aab5521a4d33fdc5b11e90995f1a2e8f58fb3447c706fc4c065657","observation_id":"845c9440-b257-492b-854c-efa610436100","resolution":{"observed_at":"2026-08-14T04:48:38.185019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.108430Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.108430Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:be7e750028372c0c49fff6e98949b63958ad7e592757fbb33634ebcb445c415e","observation_id":"6e56cf85-0996-487e-a074-6fa78b63f093","resolution":{"observed_at":"2026-08-14T04:48:34.108430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.09891","last_updated":"2017-03-29T05:58:21Z","snapshot_observed_at":"2026-08-14T21:09:28.548403Z","submitted_at":"2017-03-29T05:58:21Z","title":"LabelBank: Revisiting Global Perspectives for Semantic Segmentation","version":1},"cited_work":{"arxiv_id":"1703.09891","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.09891","snapshot_observed_at":"2026-08-14T04:48:35.504390Z","title":"LabelBank: Revisiting Global Perspectives for Semantic Segmentation","venue":"cs.CV","work_id":"282c771c-3831-4f24-9930-2887c23448de","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.112524Z"},"links":{"cited_paper":"/paper/1703.09891","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:de9c0f07c143636d954e929a99d6ce729a71759899fe8568c13d1d4efc4cc52e","observation_id":"ffff5253-70a0-4f42-96aa-1b273043ba25","resolution":{"observed_at":"2026-08-14T04:48:35.508919Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:38.092258Z","title":"Weinberger","venue":null,"work_id":"659d7d48-10c8-4604-9770-5dfd3be7349c","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.115900Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:20a25f0d43ce81a381265f988e215f0439ff681e9229210aa0a27d929d81bce6","observation_id":"05c1de69-443b-4045-bfad-cb661485c023","resolution":{"observed_at":"2026-08-14T04:48:38.170352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.03812","last_updated":"2017-12-11T15:11:01Z","snapshot_observed_at":"2026-08-14T20:04:54.728554Z","submitted_at":"2017-12-11T15:11:01Z","title":"Error Correction for Dense Semantic Image Labeling","version":1},"cited_work":{"arxiv_id":"1712.03812","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.03812","snapshot_observed_at":"2026-08-14T04:48:35.342340Z","title":"Error Correction for Dense Semantic Image Labeling","venue":"cs.CV","work_id":"2d5054ba-2fe8-49de-8c55-eddfbec449c6","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.119613Z"},"links":{"cited_paper":"/paper/1712.03812","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:fe9b02a75bc5fcfc313bad27f3afc6325318104ea111786ea097a0f4f45ff3cf","observation_id":"a4369249-409b-4f4e-8956-9dd38ea1f176","resolution":{"observed_at":"2026-08-14T04:48:35.415210Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.905573Z","title":"Scene parsing with global context embedding","venue":null,"work_id":"63638a90-25e7-4bf9-a990-94c1bca7c6d2","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.122721Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:969473bb24ae149e0ebaacc117e8e54c206cc8ca9cefe010fba34f5cff272217","observation_id":"fe82e3d3-f9bb-4c66-ab44-4bab496bb76b","resolution":{"observed_at":"2026-08-14T04:48:37.994369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.125003Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.125003Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:c3d9fe4ee36367ddd0014feb4fa93ed16ef0cbe686b7e234cf17eaaa3a0bc399","observation_id":"d879acb2-ad9c-41a6-b0d3-829382979af6","resolution":{"observed_at":"2026-08-14T04:48:34.125003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.888935Z","title":"Gated feedback reﬁnement network for dense image labeling","venue":null,"work_id":"90c5ab2f-bc4e-4053-b675-2cb9d0a28b0a","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.127547Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:032cd13e3f5190c0fb7e1ca52d80d2c0138df73d2fd992d7a26136b128635d20","observation_id":"08f1d1eb-26e0-46a2-bca1-f46ebc78fa11","resolution":{"observed_at":"2026-08-14T04:48:37.892816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.879025Z","title":"The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation","venue":null,"work_id":"deffa5e2-f86b-4b87-a355-636d1cc9b26b","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.130602Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:21cf90dca5a77a21fed3efc291b7c6604cae974ecf5b965d3e0b2ea5a3ac6313","observation_id":"c9883b1c-0695-457e-9278-63798262bd29","resolution":{"observed_at":"2026-08-14T04:48:37.882369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.869610Z","title":"Adaptive afﬁnity ﬁelds for semantic segmentation","venue":null,"work_id":"c075945e-2c0e-418b-873e-fc7201ff97c7","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.134419Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:3171bf6aec55b83cf6d6a9916b276d8087e9d127366fec634d20a4c0cf076374","observation_id":"ad6a42be-dc35-45c0-b353-cc331f1fbc26","resolution":{"observed_at":"2026-08-14T04:48:37.872252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.860323Z","title":"Recurrent scene parsing with perspective understanding in the loop","venue":null,"work_id":"7ed4f83b-12ea-4d23-9e0f-80ec65c390b2","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.220487Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:6fe29618e6f80b11cecafe7f01c60004c7e1b77e245156dec669d1a4bbedd8f6","observation_id":"8a6207ba-fd98-4d9d-81b1-833e2117429a","resolution":{"observed_at":"2026-08-14T04:48:37.863964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.850436Z","title":"Efﬁcient inference in fully connected crfs with gaussian edge potentials","venue":null,"work_id":"642655b4-96b4-4f8d-9a83-5eeb9a8ef260","year":2011},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.340333Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:2a172236b078d6f57c77df2f0a53fbf2e08d640b40c5ea4cbea60078bb871259","observation_id":"f7ec7ca8-c43d-4f2c-947e-33e97d770f24","resolution":{"observed_at":"2026-08-14T04:48:37.853484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.405198Z","title":"Imagenet classiﬁcation with deep convolutional neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.405198Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:a41165b30ea9418c646cb171ce51583e66fd8addd8a8d596a0f2044eafe146c7","observation_id":"86f12815-cf6e-4348-a3b2-b982cbe3ab4a","resolution":{"observed_at":"2026-08-14T04:48:34.405198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.675719Z","title":"Feature space optimization for semantic video segmentation","venue":null,"work_id":"558e89b0-20c8-4958-b3e5-9b6b8b681157","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.409548Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:f9cc43093f7221163b1c407bc51c7556dfe385c83e4cc4d6d1fa7df285a22166","observation_id":"55186a66-1274-42dc-b1cc-fc47b48bb597","resolution":{"observed_at":"2026-08-14T04:48:37.758583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.613573Z","title":"Semantic object parsing with graph lstm","venue":null,"work_id":"db60a698-12ad-42e9-85b8-3ebae1a36a17","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.413432Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:3a7d41dd0c8eb4ea735e3dda234a2070aa85862426e3cca2ffe12c92242c22db","observation_id":"8da4b004-a178-40b0-a03e-8c16f9a3d82b","resolution":{"observed_at":"2026-08-14T04:48:37.617836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.603690Z","title":"Semantic object parsing with local-global long short-term memory","venue":null,"work_id":"49e25a7c-e6a7-4fae-af6c-6ddb49663692","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.417656Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:06fc5694aa3e44a7390ff479a513534cd5ce04fcdc3056c7e03bbedf6f363ccc","observation_id":"e33d7bc2-62a6-4e9a-a0a7-b38df615a9c8","resolution":{"observed_at":"2026-08-14T04:48:37.607676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.593841Z","title":"Dynamic- structured semantic propagation network","venue":null,"work_id":"5135dbfa-7c57-450f-96a5-fc4fd24feab6","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.420581Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:93bb904d4de7af92084f246937db7eb27cc851f6b99ba7b33b1a173285773126","observation_id":"d744d208-4d56-4e53-bb7d-bcbf55b7ed99","resolution":{"observed_at":"2026-08-14T04:48:37.596957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.425117Z","title":"Multi-scale context intertwining for semantic segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.425117Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:9894939ce5fd1a238c693b06b9a08c8f8ff2d2a17f3675d77d320c86dba355c4","observation_id":"c3a2545b-bee2-4413-b961-44ee7092c8d1","resolution":{"observed_at":"2026-08-14T04:48:34.425117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.576434Z","title":"Reﬁnenet: Multi-path reﬁnement networks for high- resolution semantic segmentation","venue":null,"work_id":"81f30507-490e-4199-b7cd-7b8c70d12e6d","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.428405Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:89327f234b996fced20d7655bf46f300815d9e38d93d07e224c8e2b670fc264c","observation_id":"13e7125b-5fdd-4bda-a7e0-84e7c6ba13bd","resolution":{"observed_at":"2026-08-14T04:48:37.580107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.566213Z","title":null,"venue":null,"work_id":"21dfa823-aa31-4b12-a075-1b8987c222a8","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.431079Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:b47c0fff7661ee1ed13cf10aa477ccda1d222024f0abec3d2ac5ec5041243659","observation_id":"0f411673-132d-4941-8723-d353539fbca8","resolution":{"observed_at":"2026-08-14T04:48:37.569265Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.484086Z","title":"Nonparametric scene parsing: Label transfer via dense scene alignment","venue":null,"work_id":"516bd2e4-6193-4c09-b79b-b8fe86fe4e33","year":2009},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.434779Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:311cd04045f022405a7382eb750b4ee53b2ed399c0008031d458298234a5e417","observation_id":"c46c0cb9-d5c1-4441-9f85-386edba37d51","resolution":{"observed_at":"2026-08-14T04:48:37.527770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.211411Z","title":"Sift ﬂow: Dense correspondence across scenes and its applications","venue":null,"work_id":"7d4320b3-3d76-47a3-b704-586a8101841b","year":2011},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.438714Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:696fdc0576802b2e1e006d7c0e1a3ec026a8b971c0e641f67eb37f2d69672bf1","observation_id":"eb6bee52-d818-4e75-a506-457c1a8e6907","resolution":{"observed_at":"2026-08-14T04:48:37.335612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.201604Z","title":"Feature boosting network for 3d pose estimation","venue":null,"work_id":"e3c1c8f4-ffdb-4629-9f5d-2b0444c8347f","year":2019},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.446427Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:1e04943199b37a09a9327e380b036f0036d22e72e479918127f687f6ba87eb21","observation_id":"6bfcbb6b-8b60-4601-8ff2-d2658d3a0867","resolution":{"observed_at":"2026-08-14T04:48:37.205322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.189565Z","title":"Semantic image segmentation via deep parsing network","venue":null,"work_id":"5548ded7-71d9-4376-a430-4678bdb6e93a","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.450369Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:4422efbdfda8f0b933cb47e20c00392e5f3d9ac51c514b2f6fd94d7df4b565d2","observation_id":"8ec8ff5b-a2a7-4f36-a6a8-fbf33a1bce68","resolution":{"observed_at":"2026-08-14T04:48:37.194001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.180496Z","title":"Learning markov clustering networks for scene text detection","venue":null,"work_id":"0cea96b3-8dc8-4e60-ae7b-6dc68606bf36","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.455524Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:1197e4c1efb9ffbd6b4c334d4a5b151a550253d5a10ddcdefe263e062ba218b8","observation_id":"81b61773-cb04-4b08-a23a-cf86e891622f","resolution":{"observed_at":"2026-08-14T04:48:37.184049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:37.026825Z","title":"Towards robust curve text detection with conditional spatial expansion","venue":null,"work_id":"4e6dd8ab-1553-475a-9d22-cbf684d20532","year":2019},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.459169Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:2562865af505e71fea5909b99e8fec79e58fc3f918efb88d2969d5ff6b7a540f","observation_id":"992febf4-22b8-4591-8dfd-092140e23be9","resolution":{"observed_at":"2026-08-14T04:48:37.172997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.506313Z","title":"Fully convolutional networks for semantic segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.506313Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:0218e9b582b562af44b64e1aee68d5f22653e1103405c153e52cfe246f17986d","observation_id":"05040ec7-5294-4a09-a664-e13cac1ceada","resolution":{"observed_at":"2026-08-14T04:48:34.506313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.836528Z","title":"Are spatial and global constraints really necessary for segmentation? In ICCV, 2011","venue":null,"work_id":"7ee6ec5b-3059-41e9-8f97-fcac50b010fe","year":2011},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.625553Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:e6a46653520742247414c0dbb82a237c3c405d57fc44e22db6930dcb2dad25a1","observation_id":"2b83853b-1e38-4f2d-9143-14810a3bda9d","resolution":{"observed_at":"2026-08-14T04:48:36.914516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.822079Z","title":"Feedforward semantic segmentation with zoom-out features","venue":null,"work_id":"ed3fc2ee-6ac7-49f2-9c70-9227e5524cfb","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.710468Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:b33d3f572c7fe3c425ee9877266db6ff7e1d9ba643876586b4943bc93ca02ccf","observation_id":"e1fe88b6-5e06-4631-8a42-c7431a3a8f08","resolution":{"observed_at":"2026-08-14T04:48:36.825543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.713599Z","title":"The role of context for object detection and semantic segmentation in the wild","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.713599Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:48e5c30811470aec2ff06343c6d34782b4fee46725abb376924854d8a0fb9b53","observation_id":"b319f525-13e8-478c-ac47-e0ab17affe51","resolution":{"observed_at":"2026-08-14T04:48:34.713599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.621220Z","title":"Learning deconvolution network for semantic segmentation","venue":null,"work_id":"b1bbd198-88e9-4524-8aeb-7f503ac977cf","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.718690Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:d4610b38d72e4d23ec4e469fb99cf7464fcf7944118f32ea3b84e85af62f7414","observation_id":"9eb48a07-0bc6-4574-bab5-074b64d94a4d","resolution":{"observed_at":"2026-08-14T04:48:36.720863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.545103Z","title":"Large kernel matters – improve semantic segmentation by global convolutional network","venue":null,"work_id":"5011c504-3b78-4aee-8ebe-dc331e015b84","year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.721793Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:554e88bf0b42d8bc34f7483fd756520a6ebe7f287c32334bb0ce03071582c9eb","observation_id":"0cb01754-c81f-4d92-9718-59df7881955d","resolution":{"observed_at":"2026-08-14T04:48:36.548832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.533996Z","title":"Recurrent convolu- tional neural networks for scene labeling","venue":null,"work_id":"12ca5ac5-6d47-4346-92ea-97c7dfe8644e","year":2014},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.724591Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:b06a17018cbba1192d9f24a0afe0481ab553c32c9c4057a0541a41d0e4338ea1","observation_id":"1b7f9586-619b-4a44-9da5-1014ca0cc21c","resolution":{"observed_at":"2026-08-14T04:48:36.538093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.726933Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.726933Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:466dffff91f5b1fb1631884152b877c3a4ce53be617ea1a98e85fb16b54cc516","observation_id":"0bda56b4-5420-450b-8fdd-52c973cd0e5d","resolution":{"observed_at":"2026-08-14T04:48:34.726933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.518181Z","title":"Recursive context propagation network for semantic scene labeling","venue":null,"work_id":"8ae9ced3-6eae-42b1-b682-d3fbafe687d6","year":2014},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.730533Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:3255d1ce5ec061132fab7786ab8a3a4262ac9e8331757537f0d8d2a7fa91f2a1","observation_id":"7eb60104-2b0b-400d-9dd9-e1682a0fda46","resolution":{"observed_at":"2026-08-14T04:48:36.521563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.339807Z","title":"Fully convolutional networks for semantic segmentation","venue":null,"work_id":"7df0c974-d342-4f4c-95f5-029fe91681e9","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.734878Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:c8d8fe1aae0270026c5bb5c214276623300da552e83ef0058c86af15bbd986b8","observation_id":"395f01eb-b1b6-4596-8535-53e1ef5fd0a6","resolution":{"observed_at":"2026-08-14T04:48:36.455915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.232800Z","title":"Toward achieving robust low-level and high- level scene parsing","venue":null,"work_id":"cc4f021a-ffdb-4a01-b305-a496e1a24a03","year":2019},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.740331Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:5bfa5da3a54abd5c2e85915ca318316126ed648f9e1bb07a6a2c0b6c42c691e2","observation_id":"10209157-8a0e-4177-a6cf-6e4495ead889","resolution":{"observed_at":"2026-08-14T04:48:36.236200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.222806Z","title":"Scene segmentation with dag-recurrent neural networks","venue":null,"work_id":"2ff0a13e-4a19-43e1-b4a7-a1853f381851","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.743591Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:e2fcc772696160630f881ed9aed0aef9b0d6fb19461be055fc0ed1f2c7c6962d","observation_id":"842656dd-9e08-4933-b673-b6fc2210d7b4","resolution":{"observed_at":"2026-08-14T04:48:36.226288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-14T04:48:34.748490Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.748490Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:c370f9b6d176896b91a85621dcee99b4e30f8906ce26dfe419707d2c747fb1af","observation_id":"0c3d756b-b132-46d9-b8fd-fc3b56b37b91","resolution":{"observed_at":"2026-08-14T04:48:34.748490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:34.904784Z","title":"Going deeper with convolutions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.904784Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:a63acfc7616c39de4be3e136cba0fa0932be623c5edf9f3ba7a55e35a5a6cd80","observation_id":"931eaeac-19fe-4141-b907-ed3778e520b0","resolution":{"observed_at":"2026-08-14T04:48:34.904784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.205649Z","title":"Aanet: Attribute attention network for person re-identiﬁcation","venue":null,"work_id":"6b0ef25f-588d-4485-a835-b6f3b121109f","year":2019},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.971276Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:499366b66f12d348dd64f4eb71bc88268ad7961d7aff56251f297e694b484e17","observation_id":"3a90efed-97e9-445f-b1ae-f2574b1cb0b7","resolution":{"observed_at":"2026-08-14T04:48:36.210541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.099633Z","title":"Finding things: Image parsing with regions and per-exemplar detectors","venue":null,"work_id":"109aa827-9488-4bb0-92a1-a8af73266c3f","year":2013},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.975777Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:612bd436cd8fdf5316cf2d247d4207f170193b6fdf4ff2dbe653c1ddcd4dd664","observation_id":"6590a6e2-8afd-4742-b2ee-d953be39a5a0","resolution":{"observed_at":"2026-08-14T04:48:36.152581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.06885","last_updated":"2016-05-23T03:43:00Z","snapshot_observed_at":"2026-08-15T05:15:49.479330Z","submitted_at":"2016-05-23T03:43:00Z","title":"Bridging Category-level and Instance-level Semantic Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.06885","snapshot_observed_at":"2026-08-14T04:48:34.981357Z","title":"Bridging category-level and instance-level semantic image segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.981357Z"},"links":{"cited_paper":"/paper/1605.06885","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:811fd2552c0d59b1a523a28dac1af2053c3fb84aab208d524c2481cf08a5f5a1","observation_id":"5067652f-afaf-4679-afde-fe78ca1989f8","resolution":{"observed_at":"2026-08-14T04:48:34.981357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.090019Z","title":"Zoom better to see clearer: Human and object parsing with hierarchical auto-zoom net","venue":null,"work_id":"2e7bf0e8-79a8-478e-ac02-89e67e816ae6","year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.988209Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:de1e85097a90b9396b076878b1af23347fee22292de8e589b8ed7e84debeaaa0","observation_id":"6ec2f1fc-217c-444e-8648-64e05cdbfe78","resolution":{"observed_at":"2026-08-14T04:48:36.093358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1603.04871","last_updated":"2016-03-15T20:10:48Z","snapshot_observed_at":"2026-08-14T22:05:53.669161Z","submitted_at":"2016-03-15T20:10:48Z","title":"Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.04871","snapshot_observed_at":"2026-08-14T04:48:34.991090Z","title":"Combining the best of convolutional layers and recurrent layers: A hybrid network for semantic segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.991090Z"},"links":{"cited_paper":"/paper/1603.04871","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:22115e8944a8e775d66d8906a39c56cb4a9db93bb3d46193fba5b557bbe3e991","observation_id":"4ae8b2ab-3d11-4ccc-af4f-d299a93e015b","resolution":{"observed_at":"2026-08-14T04:48:34.991090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:36.077911Z","title":"Context driven scene parsing with attention to rare classes","venue":null,"work_id":"4c9d4c98-be92-4cc7-a353-8224e67cded1","year":2014},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.994212Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:46ef67db86b712965dc80197e6b35f595d50978655fd0f5e568715e273d6ed4a","observation_id":"cec7420e-d11a-4738-96f0-05af710fc298","resolution":{"observed_at":"2026-08-14T04:48:36.081835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:35.940827Z","title":"Denseaspp for semantic segmentation in street scenes","venue":null,"work_id":"98060711-b448-4cbf-9f10-b1faed308813","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:34.997284Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:1f2be77934f260ad9925439e896fe5bf33c4e00ba59feff4007cc359d39c0616","observation_id":"31b826e3-0e51-494c-97eb-866e15f324be","resolution":{"observed_at":"2026-08-14T04:48:36.012285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:35.929510Z","title":"Learning a discriminative feature network for semantic segmentation","venue":null,"work_id":"a332fd19-dfc8-45be-a654-df9f8e5adb50","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:35.000073Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:d0d493625030da8a5072c70fad5dfbe110876a35bc21d06e28db36cfd9c1ff2c","observation_id":"4bb659e7-f5f9-45bb-a8c5-1a9bc459317a","resolution":{"observed_at":"2026-08-14T04:48:35.933420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07122","last_updated":"2016-04-30T18:19:37Z","snapshot_observed_at":"2026-08-14T22:21:48.435559Z","submitted_at":"2015-11-23T07:32:14Z","title":"Multi-Scale Context Aggregation by Dilated Convolutions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07122","snapshot_observed_at":"2026-08-14T04:48:35.002941Z","title":"Multi-scale context aggregation by dilated convolutions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:35.002941Z"},"links":{"cited_paper":"/paper/1511.07122","citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:440c5ab69f68cc98c7f951eae53354ffd3777443dc06d0a0d962e4d5fbacf26d","observation_id":"1bb6565f-e6b6-4d50-b67a-4ee2b828e6cd","resolution":{"observed_at":"2026-08-14T04:48:35.002941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:35.753552Z","title":"Context encoding for semantic segmentation","venue":null,"work_id":"e2f4c9cb-1677-4452-a55e-35e28af3af2e","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:35.006553Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:6a8d395c697c39bb098d75c6332ae34ef4206f5f71532c8d7f1f26d7ee816a2c","observation_id":"9b42f97a-0766-4aa6-9b47-aff144d56970","resolution":{"observed_at":"2026-08-14T04:48:35.822120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:35.080634Z","title":"Pyramid scene parsing network","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:35.080634Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:89c78d7fc3b5c74235f6dfb24aeeb0e478bdfb4a7d66a019f1c68c9345cad75e","observation_id":"b157e4ad-e84e-4fc4-a3a3-02595d306941","resolution":{"observed_at":"2026-08-14T04:48:35.080634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:35.739090Z","title":"Psanet: Point-wise spatial attention network for scene parsing","venue":null,"work_id":"7486789c-9dd7-49f6-b7e1-225c2e33ea38","year":2018},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:35.159296Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:b3a41b3042533eb7022eb9b79b9520e10c4f4c7535743deb19e9534ef666a516","observation_id":"b6f0d098-a890-453f-af1f-8e0c4c848804","resolution":{"observed_at":"2026-08-14T04:48:35.741750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:48:35.693113Z","title":"Conditional random ﬁelds as recurrent neural networks","venue":null,"work_id":"e382924f-1a74-49d3-91a2-0cc362cd57cc","year":2015},"citing_paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-14T04:48:35.281460Z"},"links":{"citing_paper":"/paper/1909.02651"},"observation_digest":"sha256:749393db333416e7a47a0f64fb34f97be81ba8995246581afd6e00023d3602e9","observation_id":"746d3337-2eee-4df4-b82b-8194d9a121c1","resolution":{"observed_at":"2026-08-14T04:48:35.730786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1909.02651","last_updated":"2019-09-05T22:09:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T04:41:38.494750Z","submitted_at":"2019-09-05T22:09:41Z","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":4,"verified_fuzzy":54},"total_outbound_references":77},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:1909.02651."}