{"as_of":"2026-08-14T06:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9d1a0be7ac6e103e99abc110c99287bae57480642cd09f50234b13e156e2a21d","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:45:08.369833Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2412.11657/citation-record","integrity":"/paper/2412.11657/integrity","json":"/paper/2412.11657/citation-record.json","paper":"/paper/2412.11657"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:08.751790Z","title":"Deep convolutional neu- ral networks for image classification: A comprehensive re- view","venue":null,"work_id":"77f28a8d-3ea3-4835-9cb8-88e6bad7bd09","year":2017},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.262086Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:e4df2c8b52d6cce9b9c11e6eab3f4a8eac13213e60671820589dba41e6390d72","observation_id":"9ad562fa-a3a0-449d-ac8b-9ccb32ec7110","resolution":{"observed_at":"2026-08-11T14:45:08.755688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.737607Z","title":"A review of deep learn- ing in image recognition","venue":null,"work_id":"0eb2576f-254d-4673-a40e-910687fe7f38","year":2017},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.266158Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:0e5a735e1e5533cd66cfa48340dd919c8358d7fc4addf60bb70efbe9ba37b9a5","observation_id":"3f761901-e2ab-45b9-938f-2e7072d3793c","resolution":{"observed_at":"2026-08-11T14:45:08.741563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.724063Z","title":"Cuevas-Tello, Jose Nunez-Varela, Cesar Puente, and Alejandra G","venue":null,"work_id":"a199a2d1-a843-44a6-9d9b-022052120eb3","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.269750Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:b7b3e0daa3ac7a5a73c69a80e18f9db9b917c0013011b95d9ebb80a1972a4fd0","observation_id":"f4e521b1-3dcb-4ac9-b9ee-579d0d76d9d7","resolution":{"observed_at":"2026-08-11T14:45:08.728956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.711268Z","title":"Contextual convolutional neural net- works, 2021","venue":null,"work_id":"b15b5ade-5d69-4780-8704-78c7bb7eefe7","year":2021},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.273299Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:174cf724be2293e5708d056398fffc4b9af5fecc0dbfe0f428919b1b1f0737ee","observation_id":"86d364e0-c9f4-41af-b4b1-67d1b71b5331","resolution":{"observed_at":"2026-08-11T14:45:08.715888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.698123Z","title":"Noisynn: Exploring the impact of information entropy change in learning systems, 2024","venue":null,"work_id":"9fa16550-713f-449a-8256-851cf092ef6b","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.276486Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:d5356172acfa5c7555dc3f16944ea3c06447f5a45cf21ba1c149a6252be6f08c","observation_id":"4844b80e-ec91-46e7-aacc-45f4dd7bdd1e","resolution":{"observed_at":"2026-08-11T14:45:08.702744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.280218Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.280218Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:16d8772627f4a09a29fac5a9528ae7779e350d9cbead693485e02d2835871bb5","observation_id":"11127c8a-f88c-4639-8daa-3316ab4ad70e","resolution":{"observed_at":"2026-08-11T14:45:08.280218Z","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":"2026-08-11T14:45:08.677763Z","title":"Do vision trans- formers see like convolutional neural networks?, 2022","venue":null,"work_id":"d5fbd9e2-8d8e-4fb5-af26-3caac8b8357b","year":2022},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.283748Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:0ccaff5b28d15524afc5d49ee43dabc92146233612e52e03a2eb5bcdb3d5192a","observation_id":"46115417-18ad-4eb0-aad4-70e22214f80c","resolution":{"observed_at":"2026-08-11T14:45:08.681791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T14:19:26.598265Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-11T14:45:08.287213Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.287213Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:f8cd2066a5f22056ab5722dee8a2f9bfd5ded25add0f4d6d7da36bee5d808dd5","observation_id":"47aa7432-0445-4cc4-b3e1-4d4e9fa761dd","resolution":{"observed_at":"2026-08-11T14:45:08.287213Z","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":"2026-08-11T14:45:08.667368Z","title":"Squeeze-and-excitation networks, 2019","venue":null,"work_id":"d72c5215-0ac5-46eb-82aa-1690e7e22500","year":2019},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.291028Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:9404471e2ad05573bd4d2c986021f56386c037fa9ee6ca6f6a837818301a0926","observation_id":"ace7355e-7467-429e-97d6-ae4b278f9ee4","resolution":{"observed_at":"2026-08-11T14:45:08.670394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.657190Z","title":"Spatial transformer networks, 2016","venue":null,"work_id":"643c1e62-a1ed-4e9c-972f-203e4e612601","year":2016},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.294729Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:1381c9c242331eec64a4ddb284f4775b387b6feb61a8b149d8076dd54b18819a","observation_id":"291b66bc-2df0-4ae2-8336-ea45af4b4929","resolution":{"observed_at":"2026-08-11T14:45:08.660747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.645426Z","title":"Attention u-net: Learning where to look for the pancreas, 2018","venue":null,"work_id":"5a0df486-0c4c-43e7-8b6e-e49972d99b54","year":2018},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.298144Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:181af5b70ae05b01a07615a110258635c1d731a199c889f0b9bab537de674d80","observation_id":"6e18cf2b-8d49-4367-93df-b6e36aa3e6d3","resolution":{"observed_at":"2026-08-11T14:45:08.649357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.633700Z","title":"Csanet: Channel spatial attention network for robust 3d face alignment and reconstruction, 2024","venue":null,"work_id":"07e2af79-3045-4438-8809-f94a686b9f5a","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.301643Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:bfefcca7c27965cadaba1cdfc3a4c59fd939477ef5455aec65d9658e105d2379","observation_id":"8a80f2db-db99-4048-81d7-7186b0f2039f","resolution":{"observed_at":"2026-08-11T14:45:08.637316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.623280Z","title":"Ca-net: Comprehensive attention con- volutional neural networks for explainable medical image segmentation","venue":null,"work_id":"a5d87e28-02e3-4dca-a351-a0e6f7cddbcd","year":2021},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.305166Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:883653e3cc05cf9fae73b04f8a0b3bcac1b7b4da921e1414a738ea53373889d0","observation_id":"b1bc437a-66f1-440a-b4fa-074bd2ddcb1a","resolution":{"observed_at":"2026-08-11T14:45:08.626681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.613398Z","title":"Ela: Efficient local attention for deep convolutional neural networks, 2024","venue":null,"work_id":"64157169-f11d-408b-a089-b1c017b97a7e","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.308670Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:afba8fc2787459d5f6aae8c815ef60f55e173f7e06066b32530f3c105d4435f5","observation_id":"ec1b4b1c-a594-41da-89b8-3d93935571e7","resolution":{"observed_at":"2026-08-11T14:45:08.616851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.602523Z","title":"Cbam: Convolutional block attention module, 2018","venue":null,"work_id":"0a974461-412e-4952-b8c0-18e239dc1e48","year":2018},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.312476Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:9db1a671f70997caa80a522fb86001d50607cf17cc5c59e1be9d51b41a741da3","observation_id":"8c408fcb-8b5d-4ba9-9b45-aff744fe3d0e","resolution":{"observed_at":"2026-08-11T14:45:08.606207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.591258Z","title":"Cifar- 10 and cifar-100 (canadian institute for advance research)","venue":null,"work_id":"c3e1ad2c-296d-45b0-b94c-151762ad502f","year":null},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.316156Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:454e1b7d75097080955d81a9e6789632162c01c69a95e8e150684a9ab8ac80ca","observation_id":"c629cf88-fe3a-4889-afa6-29d5b0f301c9","resolution":{"observed_at":"2026-08-11T14:45:08.595402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.580238Z","title":null,"venue":null,"work_id":"febbfb34-3694-41b0-a39a-f250412c54a3","year":1958},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.319937Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:bf2532b784d07cf95a8dbe26dbe6786016fcf2853145957522da70d33ced9475","observation_id":"ee1a602a-9da9-4b5c-bd06-f6b4eb208edc","resolution":{"observed_at":"2026-08-11T14:45:08.583920Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.567776Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"28250aa0-a1cd-4b0d-9c72-2a0b9dc1f5a2","year":2009},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.323206Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:fd9385078fb9fd210b0be3cfe155f02913baf8e42af890fee13e5586ab9c64c6","observation_id":"b031e479-5d0d-445d-9671-92a3f6a0a8db","resolution":{"observed_at":"2026-08-11T14:45:08.571886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.554854Z","title":"Deep residual learning for image recognition, 2015","venue":null,"work_id":"8ee9a3f2-5223-498e-bcb1-72e648d4c392","year":2015},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.326202Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:9c22538d5ee42a6a654daf44ca9d80826c24435eb97bdd05d3f6745c50e1f343","observation_id":"0e1bc6af-6b9c-46f0-941d-1e7285e971d0","resolution":{"observed_at":"2026-08-11T14:45:08.558935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.542006Z","title":"Deep residual learning for image recognition: Cifar-10, pytorch implementation","venue":null,"work_id":"28462c40-09c3-4a39-9e7a-6edda5ccca8d","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.329424Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:c3a1d6876c4ae1cb39c9d3940912d431478ffa24795206df1c00a0e16b922efb","observation_id":"e0e3e0b8-0aef-4ec6-bde0-b74c9d2cf1cd","resolution":{"observed_at":"2026-08-11T14:45:08.546170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.529203Z","title":"MNIST handwritten digit database","venue":null,"work_id":"50f0b997-8847-47dd-8e40-e7ebbb07a8c9","year":2010},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.332471Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:2ed5f8cfcd16fd3a33fdcc741e7e3d8db7f23d6715c6b56726c772b81018a7ac","observation_id":"bfabe47f-ad4e-4cc3-bd7e-86f148f4f11f","resolution":{"observed_at":"2026-08-11T14:45:08.532882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.516893Z","title":"M ¨uller and Karla Markert","venue":null,"work_id":"a8bcaa0c-6290-4307-9044-ad1716e3ce1c","year":2019},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.335654Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:9727c27d8765d8429b1f7b8325d43a07dbf06a3161506100ca12d6a6aebe3bab","observation_id":"c11f311b-734c-495a-83a3-354f41abd050","resolution":{"observed_at":"2026-08-11T14:45:08.521392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.505567Z","title":"Proper ResNet implementation for CI- FAR10/CIFAR100 in PyTorch.https://github.com/ akamaster/pytorch_resnet_cifar10","venue":null,"work_id":"83575f17-3e64-4a54-9b69-5900458cd52b","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.338687Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:adcd1002d93d712df9409b532db6c87421494d83b225ba75912c1502d28f79b0","observation_id":"e7e1fc15-4ad5-4539-a2a1-4dbbc877de66","resolution":{"observed_at":"2026-08-11T14:45:08.509169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02391","last_updated":"2019-12-03T02:13:03Z","snapshot_observed_at":"2026-08-11T11:40:41.424159Z","submitted_at":"2016-10-07T19:54:24Z","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02391","snapshot_observed_at":"2026-08-11T14:45:08.341765Z","title":"Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Ba- tra","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.341765Z"},"links":{"cited_paper":"/paper/1610.02391","citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:70a9e13c0e7f56818a2bd95657d84518b04819575e1b8c91884a39a675d53c83","observation_id":"134ab9d5-abdc-4913-a709-d5594ca44697","resolution":{"observed_at":"2026-08-11T14:45:08.341765Z","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":"2026-08-11T14:45:08.494280Z","title":"CNNten- tion Github Repository","venue":null,"work_id":"3137a398-4601-4f3b-b43b-d555d862f782","year":2024},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.345967Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:df4b04d48a1b456df3e3f2d5242e4ebd0ebf081cbaa0d3062c27752078ba9ed7","observation_id":"3a029942-f877-42a5-8a19-7b4a803bf0a7","resolution":{"observed_at":"2026-08-11T14:45:08.498110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-11T14:45:08.349638Z","title":"Gomez, Lukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.349638Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:cf64756eece5abc50e2cfde4702b8367e1e38f1f0babed4a796e8dc119d27e57","observation_id":"67a93661-1f62-46d3-a66a-eabd5f0460f9","resolution":{"observed_at":"2026-08-11T14:45:08.349638Z","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":"2026-08-11T14:45:08.353535Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.353535Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:54d7f7bbc25b6d7eecf9e25c007c5476bbdf5b0954feaa4db598651d7c158c66","observation_id":"e093fb78-40ca-4403-8f97-e2944d087a3b","resolution":{"observed_at":"2026-08-11T14:45:08.353535Z","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":"2026-08-11T14:45:08.477134Z","title":"Self-attention generative adversarial networks,","venue":null,"work_id":"38e23b72-1eb3-4924-b43a-cb53066515c9","year":null},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.357204Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:5034a8e4cca1b98fed2faf81633d009a39a2321fd8bdfd0ffa7dad4870e8a0ab","observation_id":"2aa709c6-4b51-4054-b6f5-5d46b17517e3","resolution":{"observed_at":"2026-08-11T14:45:08.480921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.465101Z","title":"Mlflow: An open source platform for the ma- chine learning lifecycle","venue":null,"work_id":"e71034ba-7b8d-4a7f-853d-e1461b32d043","year":null},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.360818Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:a0767481ccfccedfcd93928fe00ce06b548edbd18166bd48a650fc6f4a08e78e","observation_id":"e131d119-c917-4ce9-889e-074564ea35a2","resolution":{"observed_at":"2026-08-11T14:45:08.469060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08831","last_updated":"2016-05-28T02:13:32Z","snapshot_observed_at":"2026-07-06T04:57:52.963878Z","submitted_at":"2016-05-28T02:13:32Z","title":"Weighted Residuals for Very Deep Networks","version":1},"cited_work":{"arxiv_id":"1605.08831","doi":null,"metadata_source":"pith","pith_arxiv_id":"1605.08831","snapshot_observed_at":"2026-08-11T14:45:08.404433Z","title":"Weighted Residuals for Very Deep Networks","venue":"cs.CV","work_id":"d1d58daa-a84b-4c21-ba8b-5caed2fa86a6","year":2016},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.364956Z"},"links":{"cited_paper":"/paper/1605.08831","citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:27bc292fe8befa086192f7aa0db7a4c5f7c7f8a4ac5aa958e4460f21f5e44bf9","observation_id":"84a8585d-79c4-4f68-a07c-073b29c858a9","resolution":{"observed_at":"2026-08-11T14:45:08.409815Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:45:08.453152Z","title":"Going deeper with convolutions, 2014","venue":null,"work_id":"5bbae513-e7c5-463f-a0ae-3d54aa91e9ad","year":2014},"citing_paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:08.369833Z"},"links":{"citing_paper":"/paper/2412.11657"},"observation_digest":"sha256:f85580117e60ad405b737b51cd3a2ca63e7fdb536edb9c8ecb025a9586b1841a","observation_id":"a4172184-fbd4-4101-82c2-ec0f501e9d75","resolution":{"observed_at":"2026-08-11T14:45:08.457306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11657","last_updated":"2024-12-30T14:39:08Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T14:40:52.438786Z","submitted_at":"2024-12-16T11:00:02Z","title":"CNNtention: Can CNNs do better with Attention?"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":24},"total_outbound_references":31},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.11657."}