{"as_of":"2026-08-17T18:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1a56807ea977d4e097813dff96610465ca898bdb80fe813a7232c87605c7152c","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:02:06.628790Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/1908.01958/citation-record","integrity":"/paper/1908.01958/integrity","json":"/paper/1908.01958/citation-record.json","paper":"/paper/1908.01958"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-14T15:02:06.398525Z","title":"Layer normalization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.398525Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:cbab2979f173c5019081a5ce4d3a408e579f7644232681e441c11902a6c79978","observation_id":"41fc94c9-2beb-4e47-bc13-f7af37f88f93","resolution":{"observed_at":"2026-08-14T15:02:06.398525Z","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":"2026-08-14T15:02:07.499662Z","title":"Gift: A real-time and scalable 3d shape search engine","venue":null,"work_id":"f0b293b6-a2f2-4306-9fb5-de9b09cfa18c","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.403222Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:5e005744be92a138fd956930fff6c9dff51510eaf6409367cc15ae9448fe779c","observation_id":"be835883-1fb1-415d-88ae-1e5a668b5065","resolution":{"observed_at":"2026-08-14T15:02:07.504967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.482912Z","title":"Re-ranking via metric fusion for object retrieval and per- son re-identification","venue":null,"work_id":"6100179e-99f0-491c-8e0f-c1492389799f","year":2019},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.407342Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:2fee4cdef14b4557a437df27579e83ff52ef2ffbbdc7d875cf7e6cc8f18ce308","observation_id":"29722c81-cfd7-4f0d-a608-00d5c8e9c736","resolution":{"observed_at":"2026-08-14T15:02:07.489955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.469657Z","title":"Ensemble diffusion for retrieval","venue":null,"work_id":"fabdceea-995f-4b6b-83bd-cd06e87bbfd3","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.411567Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:dbe2f078d3e12c27a4734b332a96cb96114ec15ff53417de2a8aa2498787497b","observation_id":"2e51302b-d8ea-40f3-85f0-6e95837e95a0","resolution":{"observed_at":"2026-08-14T15:02:07.474258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.455557Z","title":"Shape con- text: A new descriptor for shape matching and object recog- nition","venue":null,"work_id":"7974d38b-b0f2-4585-8b65-dfd6bab343ea","year":2001},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.415717Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:0b5099b28c55455cdf1d938c05a28b67eb5391ba61cc0a2fb46b91ed9fcfd244","observation_id":"d1600e86-0b07-4525-bc3d-f54fa15e8c0b","resolution":{"observed_at":"2026-08-14T15:02:07.461030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.441102Z","title":"3d medical objects retrieval approach using spharms descriptor and network flow as sim- ilarity measure","venue":null,"work_id":"f0385b6b-bfdc-44fc-9cb9-627e2252080a","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.419854Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:f95a22d19bd6125a90434bc61a57ed4539d109ad9346a3861fc4a44fbbafe0b0","observation_id":"e6038d24-ba40-4e2d-862f-bfc8253271b3","resolution":{"observed_at":"2026-08-14T15:02:07.445299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.429051Z","title":"Bronstein, Michael M","venue":null,"work_id":"013c5e4c-f449-4dc4-b18e-cc7695734e12","year":2011},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.423913Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:fd362a2d0c3773948b71755d140f443014507ce0c352ddf75f0cceb829306381","observation_id":"ceddeb34-a0ea-496d-8304-06242bff9de2","resolution":{"observed_at":"2026-08-14T15:02:07.433014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-15T22:26:43.274625Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-08-14T15:02:06.429170Z","title":"Shapenet: An information-rich 3d model repository","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.429170Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:fb78925a52ea001a7efa4f88f66a7f9f82c288e9166783b304d48288fd476891","observation_id":"82d9ff7b-edca-494a-aa3b-ff656325402a","resolution":{"observed_at":"2026-08-14T15:02:06.429170Z","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":"2026-08-14T15:02:07.415823Z","title":"Group consistent similarity learning via deep crf for person re-identification","venue":null,"work_id":"3c975c74-f254-436f-b0e8-19c72484c1eb","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.433426Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:6a28005d1105390beb899612b3e756746031b84d55f6261df0b0da983dc0b8a0","observation_id":"c4a08e84-61d5-4584-a2d7-1f9d0e27e604","resolution":{"observed_at":"2026-08-14T15:02:07.421073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.401467Z","title":"On visual similarity based 3d model retrieval","venue":null,"work_id":"dcceab8e-da31-4f55-9356-8a304c16cad4","year":2003},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.437091Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:92c7ea9f0a8991db21123fb366c2ed29d9d444496aa94373cc28344cca2bb858","observation_id":"1ea1b338-41de-47d2-9f20-2b5d1c06a06c","resolution":{"observed_at":"2026-08-14T15:02:07.406478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.386920Z","title":"Veram: View-enhanced recurrent attention model for 3d shape classification","venue":null,"work_id":"161386a1-f466-4e3a-8768-28c176e9cc76","year":null},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.440912Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:1493b7d30e666833839a228ec93afdc9313da758aa3ce18add886049e975fa9e","observation_id":"1e16eaf4-5359-415e-9935-d1e3db783961","resolution":{"observed_at":"2026-08-14T15:02:07.392589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.371471Z","title":"Siamese cnn-bilstm ar- chitecture for 3d shape representation learning","venue":null,"work_id":"25a55820-7ab0-4d09-804b-c94c3fe638a4","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.445614Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:899ac2f33088f5dbc3a3f6d411dbccc3cbbb2a1cdbfd1052a4889991c2b24476","observation_id":"aa914cd1-ce4e-4443-bbca-241198ae20e3","resolution":{"observed_at":"2026-08-14T15:02:07.376200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.355171Z","title":"3d deep shape descriptor","venue":null,"work_id":"46c7a046-5dd6-4eeb-899b-e97e5cdc40b5","year":2015},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.449359Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:214c86e15f58fe3c4d4d372a39a2af58d75e5c5faa0b9b7552e92081ac767c72","observation_id":"9a1dc20f-ead4-497f-a3df-1906ed3b45d2","resolution":{"observed_at":"2026-08-14T15:02:07.361370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.341986Z","title":"Gvcnn: Group-view convolutional neural networks for 3d shape recognition","venue":null,"work_id":"9e01915c-a03a-4a98-ba4c-cb77009ef2fb","year":null},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.453201Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:ee3b43a26c5ebf6b0c7126313119832027e3212bc086a6cc87c0107f735c7ca4","observation_id":"7cad947a-a682-4b39-b829-4bbbb33eca13","resolution":{"observed_at":"2026-08-14T15:02:07.346555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.326985Z","title":"Deep aggregation of local 3d geometric features for 3d model retrieval","venue":null,"work_id":"94714406-8eca-464c-91c9-282a332cbdd5","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.457258Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:338046735819f3f4c120efdbe736d34c4caff01e46cb3c95f031f8e975fa0438","observation_id":"d8b620df-0027-4794-abf3-4d48fbcf2d83","resolution":{"observed_at":"2026-08-14T15:02:07.332180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.306359Z","title":"3d object retrieval with bag-of-region-words","venue":null,"work_id":"e5623618-cd2d-4e7a-884d-59b558015cf6","year":2010},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.461133Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:d061ef71912c5d7d8cb6c78fa51870bfed86f809669c39bb625c67c7dd7bea1f","observation_id":"da268cad-7573-4af9-92c0-ad844f9ad306","resolution":{"observed_at":"2026-08-14T15:02:07.314724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.287342Z","title":"Seqviews2seqlabels: Learning 3d global features via aggregating sequential views by rnn with atten- tion","venue":null,"work_id":"4b7f4cd7-bbc4-4348-b446-c0df426027d5","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.466905Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:307d62be9c9381c08fc41297c3c97dc4c5f1d2ae1b8680be8d78a66b5f91afd3","observation_id":"487b344e-d099-46e1-9b28-cb5defbf14e6","resolution":{"observed_at":"2026-08-14T15:02:07.293848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.271061Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"d4933015-a747-4abf-a782-16de832cf89d","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.470645Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:1f287cc03cc4070cf1cd198c95790e75a2fa573cfa3d84938644abe693043746","observation_id":"ba9d68bd-be62-4a97-96bd-ac2dec7f4863","resolution":{"observed_at":"2026-08-14T15:02:07.275024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.256985Z","title":"Triplet-center loss for multi-view 3d object retrieval","venue":null,"work_id":"627db114-cc76-4c13-933f-0ffb71500b6c","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.474116Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:ae7c76d3bd3222462240f56974351734a5a6ef632ccf910715e7d5b5cb92daa8","observation_id":"2274716f-40ce-43f1-a609-8f746b1c9973","resolution":{"observed_at":"2026-08-14T15:02:07.261548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.243525Z","title":"Interactive retrieval of 3d virtual shapes using physical objects","venue":null,"work_id":"f5da6ed9-ecfe-4eff-b176-7db8fb6548ef","year":2004},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.477564Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:c58ec94fc660586688904ac8681b826d14281e63749c678d7c010c56f3864661","observation_id":"aafe767d-d7f9-42f6-aa67-cd0372bf6992","resolution":{"observed_at":"2026-08-14T15:02:07.248432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.5903","last_updated":"2015-04-10T15:36:01Z","snapshot_observed_at":"2026-08-14T23:06:58.209407Z","submitted_at":"2014-12-18T15:49:46Z","title":"Deep Structured Output Learning for Unconstrained Text Recognition","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.5903","snapshot_observed_at":"2026-08-14T15:02:06.481590Z","title":"Deep structured output learning for uncon- strained text recognition","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.481590Z"},"links":{"cited_paper":"/paper/1412.5903","citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:ac27385e621b0ac5dfdd82fc80d3bad0807cdaf57d33d3c118bd907100323f52","observation_id":"02a130ae-d03c-4b83-8d29-99cbdd7035ba","resolution":{"observed_at":"2026-08-14T15:02:06.481590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.02410","last_updated":"2016-02-11T23:01:48Z","snapshot_observed_at":"2026-08-14T22:11:20.076826Z","submitted_at":"2016-02-07T19:11:17Z","title":"Exploring the Limits of Language Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.02410","snapshot_observed_at":"2026-08-14T15:02:06.485989Z","title":"Exploring the limits of language modeling","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.485989Z"},"links":{"cited_paper":"/paper/1602.02410","citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:40da3113c5c391d583a359105579cd4c773c50f723e9e5c909b5ec0406e6de06","observation_id":"f9f62888-cbea-4287-b77c-db3f0cf9adf5","resolution":{"observed_at":"2026-08-14T15:02:06.485989Z","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":"2026-08-14T15:02:07.227847Z","title":"Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints","venue":null,"work_id":"b0ed5fbb-773c-4ec1-9a90-f1bcdf37a102","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.490220Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:45bcd2c491f1ea126ff1e91b5e124759a44df0b1ab28835b28cb7f806563a014","observation_id":"790efaf8-1076-43be-91c4-e650850fb6b0","resolution":{"observed_at":"2026-08-14T15:02:07.232140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.211447Z","title":"Rotation invariant spherical harmonic repre- sentation of 3 d shape descriptors","venue":null,"work_id":"f9474540-74c8-413f-a776-92e5df8a5dbf","year":2003},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.493935Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:b74dab0d511b207b8a8d866aa54475a8640c9de471e992c184563cec09dbee7c","observation_id":"13457967-0ff8-4d07-98f1-095d4e2ce999","resolution":{"observed_at":"2026-08-14T15:02:07.217286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.191578Z","title":"Statistical frame- work for model-based image retrieval in medical applica- tions","venue":null,"work_id":"e438b9a3-5145-49e6-8342-5edc45036260","year":2003},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.498053Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:78889dda8c9e89eb2d2f8baeb9510d0ddcb9b959544523897480e24a20a15baf","observation_id":"ba27918d-2f98-4553-aec0-af8e3893dd58","resolution":{"observed_at":"2026-08-14T15:02:07.198938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.172431Z","title":"Anatomical 2d/3d shape-matching in virtual reality: A user interface for quantifying joint kinematics with radiographic imaging","venue":null,"work_id":"27918b6d-8396-4006-9abf-0663e013e2d8","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.501866Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:23a8422ca8bf53b6e5be63241f6b3b589332d22f28d43aa606dc95774b5e21eb","observation_id":"2774ee1f-e201-481e-934b-f2837d271bd1","resolution":{"observed_at":"2026-08-14T15:02:07.177742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1408.5882","last_updated":"2014-09-03T03:09:02Z","snapshot_observed_at":"2026-08-14T23:21:58.977221Z","submitted_at":"2014-08-25T19:48:04Z","title":"Convolutional Neural Networks for Sentence Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1408.5882","snapshot_observed_at":"2026-08-14T15:02:06.505927Z","title":"Convolutional neural networks for sentence clas- sification","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.505927Z"},"links":{"cited_paper":"/paper/1408.5882","citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:0b69dceb7796849575f60fbe87be54baa9f1dee997c89d84b6184f5925c37eb6","observation_id":"7ef96ae7-1937-4dcc-a6a1-1a4f283e6513","resolution":{"observed_at":"2026-08-14T15:02:06.505927Z","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":"2026-08-14T15:02:07.157814Z","title":"Character-aware neural language models","venue":null,"work_id":"7505e616-c2ca-4f97-a5cf-00a7825bbb94","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.510873Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:390f0a21f6d43ac09c2f1bc4a84c4855d12781831e773581d90caec22d3b9c31","observation_id":"427e9f56-841f-419c-9781-7468ffe4f811","resolution":{"observed_at":"2026-08-14T15:02:07.162966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.144444Z","title":"Escape from cells: Deep kd-networks for the recognition of 3d point cloud mod- els","venue":null,"work_id":"a11b11f5-325f-4340-ae03-a28469f33530","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.516110Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:3f50442c6143d5c2933e8c03b8b088e5493b7ab19d6fb77c2dc9909fc292c1d1","observation_id":"b50c884a-0aa4-4e50-8d44-611896ae6445","resolution":{"observed_at":"2026-08-14T15:02:07.149151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.125974Z","title":"Imagenet classification with deep convolutional neural net- works","venue":null,"work_id":"b4225d4c-33fd-4f33-a32d-2f9448565b4d","year":2012},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.520012Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:589e7c6c0ebad8c0677a1912d9453feb4a51326b5c0dff1a885b5fe126e19b16","observation_id":"c3dfb4d0-3b1e-42e8-94bc-51389dfbf533","resolution":{"observed_at":"2026-08-14T15:02:07.131248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.110795Z","title":"Learning discriminative 3d shape representations by view discerning networks","venue":null,"work_id":"375fa557-ab26-4284-a603-c2e07cd1c3bf","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.523596Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:b7c16afece0f1699b990c57084b5e493914652e42ab490ce804bf83335f3c7b9","observation_id":"519b05e7-e939-493c-bb76-21126a0a8ae6","resolution":{"observed_at":"2026-08-14T15:02:07.115798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.094034Z","title":"Weighted neural bag-of-n-grams model: New baselines for text classification","venue":null,"work_id":"20beacf3-a7ee-4cad-9112-75df137811fd","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.527574Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:5e92a5c58e6b705ed80abe083ff6e3564dfe3d9c23de8ed5f6790d48adfda17e","observation_id":"2adb42f7-f112-4735-a61f-67f8e98ee08b","resolution":{"observed_at":"2026-08-14T15:02:07.100852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.073465Z","title":"N-gram-based machine translation","venue":null,"work_id":"c9fc448e-6d80-4607-b942-875d6c4aa2a8","year":2006},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.531559Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:a818bf6d306ddc3b44d5da87e75b5ba2d527a0cb4f661029985a9d8a3e05f585","observation_id":"113a851a-773c-4120-84df-d5da98a24be6","resolution":{"observed_at":"2026-08-14T15:02:07.079136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.057326Z","title":"V oxnet: A 3d con- volutional neural network for real-time object recognition","venue":null,"work_id":"ec9374db-9e34-4b96-bb09-9559ab6a32af","year":2015},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.535111Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:d1a3a8996607e6ea364e62addee9cc77fcceafbd6c8f96ea5e32cc03f6fb37c3","observation_id":"1893d814-ccdc-4975-a1e9-d0e51d193767","resolution":{"observed_at":"2026-08-14T15:02:07.062043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.539160Z","title":"Automatic differentiation in pytorch","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.539160Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:658abd77605691aaf3c73617b863d8e54f24d85e763d2707f97466c4a01af214","observation_id":"19bf74ef-099a-4562-9e69-8f2691f8a3a8","resolution":{"observed_at":"2026-08-14T15:02:06.539160Z","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":"2026-08-14T15:02:07.033090Z","title":"Cnn-n-gram for handwriting word recognition","venue":null,"work_id":"03a7d054-0f0d-43b8-ace4-7ad80717fcf9","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.543049Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:aca19662f7cd56bca2fe11d188be80b690f5f33c9235cb611904aa9658a1c5bb","observation_id":"8f88b121-4053-4bc2-b015-ae4a09b5f2c5","resolution":{"observed_at":"2026-08-14T15:02:07.038461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.019020Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":"f67e18dc-7da8-4f7f-b995-21a9c324b02c","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.546861Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:b2fe0e8b53b50a15acbf2506ffba0d696101fab1017b67f4112c7e45504ee305","observation_id":"aea7c94a-e2b1-484a-b67d-cba21feb41e9","resolution":{"observed_at":"2026-08-14T15:02:07.023809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:07.004461Z","title":"V olumetric and multi-view cnns for object classification on 3d data","venue":null,"work_id":"81a092be-a0a1-4c6c-a1da-ae10f93afd6f","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.550791Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:328637337ae993090571694ee022b9272bdcdbd8cf649567cbe52d6d1cd10c00","observation_id":"2cc9edc6-22de-4fc0-8a94-85870bcadf88","resolution":{"observed_at":"2026-08-14T15:02:07.009200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.990771Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":"f95d6b05-7a89-4155-9043-e3f6aa389943","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.555154Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:ff17bd4a18a8906d451614f624664878f73b7c406314e56ccb9eeec834b9a9b0","observation_id":"53bbc235-8b17-49b3-be7c-ef25a1d23452","resolution":{"observed_at":"2026-08-14T15:02:06.995000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.976821Z","title":"Discrimi- native n-gram language modeling","venue":null,"work_id":"a972b906-29f9-4115-91a2-ed566648af87","year":2007},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.558699Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:6b478151b1dc7c799dcd7fbe2cb015cec4bb5ed294558997a0670a140a78c657","observation_id":"f030094b-c27d-4491-b379-cb2c764b541a","resolution":{"observed_at":"2026-08-14T15:02:06.981834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.959340Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":"73042b0a-d9ce-4280-8c63-5df0e8f0422a","year":2015},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.562701Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:591454051513c7a11c5a4005092321b9b978905b80b14e2646d5e7a816c5da67","observation_id":"c298c7ff-548b-4cc2-af90-36584377ddf5","resolution":{"observed_at":"2026-08-14T15:02:06.965218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.944533Z","title":"Shrec16 track: largescale 3d shape retrieval from shapenet core55","venue":null,"work_id":"9b999328-61b9-4850-ba41-cf61b5a0e272","year":2016},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.566352Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:74fb65feceab30dda1a5edd36b5a1cb67de3720c766d6313753260a21bfe7c2d","observation_id":"239e6212-049e-4aa6-ae48-d52970ca7487","resolution":{"observed_at":"2026-08-14T15:02:06.949387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.928454Z","title":"Ensemble of panorama-based convolutional neural networks for 3d model classification and retrieval","venue":null,"work_id":"40d297b3-44f2-41b4-875c-1f047534dbfe","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.569919Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:b30717eab09b2d02858b55321cb6ac6a4e02a74f33a2a76be0c1a7d618810cdf","observation_id":"730b318e-da02-46d3-afe3-27073125a307","resolution":{"observed_at":"2026-08-14T15:02:06.933622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.914147Z","title":"Deeppano: Deep panoramic representation for 3-d shape recognition","venue":null,"work_id":"b60aa7ae-9aab-46a9-9118-9edcdb0da977","year":null},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.574169Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:81d46f6e0967cb83e54dcf2fedec9abc93599b6bab6ea008dcb38b18921f3c2c","observation_id":"9896c39b-bad4-4042-972e-85eaeabf1d56","resolution":{"observed_at":"2026-08-14T15:02:06.919455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.578314Z","title":"Very deep convo- lutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.578314Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:4d8b96de8b95bd2e92a52c2d4dce3bffd59118214f82294f797fc921514d8f1a","observation_id":"67446799-1b14-44eb-8aaf-e12aa159921b","resolution":{"observed_at":"2026-08-14T15:02:06.578314Z","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":"2026-08-14T15:02:06.900873Z","title":"Multi-view convolutional neural networks for 3d shape recognition","venue":null,"work_id":"b6e04a71-5e77-4d8c-b644-5e31aedfbbab","year":null},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.582637Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:eea5681df696523cd95daf1cfadf804a0672fd5748c8059857c3dc7aabdd2ef8","observation_id":"3a438989-6ea1-4264-9415-3ec01a7386ab","resolution":{"observed_at":"2026-08-14T15:02:06.905249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.886276Z","title":"Going deeper with convolutions","venue":null,"work_id":"b0bd6c4a-c920-4a30-aa24-426e4b4923b8","year":2015},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.587254Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:010ca3a15191a0b68474507140b230f288920750d835730a4ce9e1d69cd5a711","observation_id":"faa59de7-e191-4496-9dc8-7d6f02168c64","resolution":{"observed_at":"2026-08-14T15:02:06.891305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.871370Z","title":"Outdoors aug- mented reality on mobile phone using loxel-based visual fea- ture organization","venue":null,"work_id":"c7683f62-22d6-4903-a1b9-4309a33a57e5","year":2008},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.591060Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:95ea80dfde7cba951ba8af2c2b598455e362cbf41b806791042d30dd7ce5a46b","observation_id":"5b43673e-7ca4-4a8b-9b2a-cba72c3f766e","resolution":{"observed_at":"2026-08-14T15:02:06.876052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.857001Z","title":"3d print- ing of cardiac structures from medical images: an overview of methods and interactive tools","venue":null,"work_id":"0c6fe86d-4284-4128-bdfc-5dd8b73657d4","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.599095Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:6b456e4c83c1f62332445766caac6149551443638caf80c398b95bb9bbcf0b49","observation_id":"4a453a14-22ba-421e-b81c-20bcfa6451d8","resolution":{"observed_at":"2026-08-14T15:02:06.861857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.844078Z","title":"Attention is all you need","venue":null,"work_id":"843f0776-2198-435e-9f0d-f05c5d884bb1","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.603075Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:f43462c87da56321e174572c8ff55736e672e8568a57f8f3597e4f69e74219c9","observation_id":"1708cee6-90f0-4a22-bb33-e8670349bc8e","resolution":{"observed_at":"2026-08-14T15:02:06.848944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.818284Z","title":"Dominant set clustering and pooling for multi-view 3d object recogni- tion","venue":null,"work_id":"f36edf46-6589-4fa6-85f7-46e7aea4d43c","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.606816Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:e3896a7ee0c2d657e0877ac95c8f74b6a2e203dda1b739040543a7a3919b7add","observation_id":"c15dc951-cdbf-4d65-a785-2dd78afd1906","resolution":{"observed_at":"2026-08-14T15:02:06.825931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.799853Z","title":"O-cnn: Octree-based convolutional neu- ral networks for 3d shape analysis","venue":null,"work_id":"ba9f691b-5c7a-4111-a95e-a9ebebe1847f","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.612224Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:61d9289e477688acc2bd6b92af7847602d4dc61fc2a124553f33242efb585c24","observation_id":"4461ae85-7c6a-4432-8d57-217e98c8fe6a","resolution":{"observed_at":"2026-08-14T15:02:06.805169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.784866Z","title":"3d shapenets: A deep representation for volumetric shapes","venue":null,"work_id":"5ede7549-df92-4f09-92f4-49b09d8d91ae","year":1912},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.616246Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:d8fe8a2c9e917aa58b218c6ae7ec04e6f9974c881a3fc0311a53cb457aaf20d7","observation_id":"52978dee-eb95-464b-be50-e766c5a2b4d1","resolution":{"observed_at":"2026-08-14T15:02:06.790846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.762893Z","title":"Deepshape: Deep-learned shape descriptor for 3d shape retrieval","venue":null,"work_id":"e224d441-76b3-40ec-a741-eddec643ddd2","year":2017},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.620343Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:13919f0490777f7508ef23439b83c0b8204f6067205d516aa2ccff8f35449746","observation_id":"3793e3b5-b6c1-41fd-ba39-b5f018087bbd","resolution":{"observed_at":"2026-08-14T15:02:06.768907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.747722Z","title":"Multi-view har- monized bilinear network for 3d object recognition","venue":null,"work_id":"ff2741b5-3a45-43b8-b1dd-c1f63f882700","year":2018},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.624560Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:a1c797aae360f268432c6492af1f2f40e8705d50ecd8f98b628c3fe3261be7b0","observation_id":"a4aa60a7-c263-4d7d-b637-1d8dbc95c6cb","resolution":{"observed_at":"2026-08-14T15:02:06.752797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T15:02:06.729648Z","title":"Semi-supervised 3d ab- dominal multi-organ segmentation via deep multi-planar co- training","venue":null,"work_id":"3351078c-35c3-4c20-ac69-40cb65028c74","year":2019},"citing_paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T15:02:06.628790Z"},"links":{"citing_paper":"/paper/1908.01958"},"observation_digest":"sha256:06ea7b6e3e1e8330d622ae762eae3c3c4cdc4a5f759f0e80edd03c9bca20c3ce","observation_id":"054694ee-436f-4e40-b5c6-2ae2dd151376","resolution":{"observed_at":"2026-08-14T15:02:06.736146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.01958","last_updated":"2019-08-15T14:30:34Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T14:56:07.470207Z","submitted_at":"2019-08-06T05:03:53Z","title":"View N-gram Network for 3D Object Retrieval"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":49},"total_outbound_references":56},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:1908.01958."}