{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UI7AR2BPEWXZE5B7VR2O6GCJ3E","short_pith_number":"pith:UI7AR2BP","schema_version":"1.0","canonical_sha256":"a23e08e82f25af92743fac74ef1849d9299092e10ff1d591d7918d2d1088dd30","source":{"kind":"arxiv","id":"2105.09448","version":2},"attestation_state":"computed","paper":{"title":"Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Abheesht Sharma, Gunjan Chhablani, Harshit Pandey, Tirtharaj Dash","submitted_at":"2021-05-20T01:25:42Z","abstract_excerpt":"Superpixels are higher-order perceptual groups of pixels in an image, often carrying much more information than the raw pixels. There is an inherent relational structure to the relationship among different superpixels of an image such as adjacent superpixels are neighbours of each other. Our interest here is to treat these relative positions of various superpixels as relational information of an image. This relational information can convey higher-order spatial information about the image, such as the relationship between superpixels representing two eyes in an image of a cat. That is, two eye"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2105.09448","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-05-20T01:25:42Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"1bf69fb82a179ec21ca00dc3c904127ed66fedbf8f4a42a248c1112e696fbc00","abstract_canon_sha256":"399df6c381f9715eb57a8148a3544a6e5fa66afa9a380714f1c5d2e48cd45a0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:55.371555Z","signature_b64":"m4K1+6ZHxkMyHeLDS16Y7/1xW80g7wdFE0HY5Kc2ZhHiea8EFAVkQxU2FFGleaLyfGEMWYxC2ZFtbC7LslnfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a23e08e82f25af92743fac74ef1849d9299092e10ff1d591d7918d2d1088dd30","last_reissued_at":"2026-07-05T04:24:55.371084Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:55.371084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Abheesht Sharma, Gunjan Chhablani, Harshit Pandey, Tirtharaj Dash","submitted_at":"2021-05-20T01:25:42Z","abstract_excerpt":"Superpixels are higher-order perceptual groups of pixels in an image, often carrying much more information than the raw pixels. There is an inherent relational structure to the relationship among different superpixels of an image such as adjacent superpixels are neighbours of each other. Our interest here is to treat these relative positions of various superpixels as relational information of an image. This relational information can convey higher-order spatial information about the image, such as the relationship between superpixels representing two eyes in an image of a cat. That is, two eye"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.09448","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.09448/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2105.09448","created_at":"2026-07-05T04:24:55.371139+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.09448v2","created_at":"2026-07-05T04:24:55.371139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.09448","created_at":"2026-07-05T04:24:55.371139+00:00"},{"alias_kind":"pith_short_12","alias_value":"UI7AR2BPEWXZ","created_at":"2026-07-05T04:24:55.371139+00:00"},{"alias_kind":"pith_short_16","alias_value":"UI7AR2BPEWXZE5B7","created_at":"2026-07-05T04:24:55.371139+00:00"},{"alias_kind":"pith_short_8","alias_value":"UI7AR2BP","created_at":"2026-07-05T04:24:55.371139+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.06869","citing_title":"Safety Monitoring of Machine Learning Perception Functions: a Survey","ref_index":70,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E","json":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E.json","graph_json":"https://pith.science/api/pith-number/UI7AR2BPEWXZE5B7VR2O6GCJ3E/graph.json","events_json":"https://pith.science/api/pith-number/UI7AR2BPEWXZE5B7VR2O6GCJ3E/events.json","paper":"https://pith.science/paper/UI7AR2BP"},"agent_actions":{"view_html":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E","download_json":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E.json","view_paper":"https://pith.science/paper/UI7AR2BP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.09448&json=true","fetch_graph":"https://pith.science/api/pith-number/UI7AR2BPEWXZE5B7VR2O6GCJ3E/graph.json","fetch_events":"https://pith.science/api/pith-number/UI7AR2BPEWXZE5B7VR2O6GCJ3E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E/action/storage_attestation","attest_author":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E/action/author_attestation","sign_citation":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E/action/citation_signature","submit_replication":"https://pith.science/pith/UI7AR2BPEWXZE5B7VR2O6GCJ3E/action/replication_record"}},"created_at":"2026-07-05T04:24:55.371139+00:00","updated_at":"2026-07-05T04:24:55.371139+00:00"}