{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DKXXJCIN5HOYUWXAND3XU5GNYA","short_pith_number":"pith:DKXXJCIN","schema_version":"1.0","canonical_sha256":"1aaf74890de9dd8a5ae068f77a74cdc000e270b231ac3049ab97e42cb183e261","source":{"kind":"arxiv","id":"2401.15362","version":1},"attestation_state":"computed","paper":{"title":"Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayush Dubey, Satish Kumar Singh, Shiv Ram Dubey, Wei-Ta Chu","submitted_at":"2024-01-27T09:39:11Z","abstract_excerpt":"Unsupervised image retrieval aims to learn the important visual characteristics without any given level to retrieve the similar images for a given query image. The Convolutional Neural Network (CNN)-based approaches have been extensively exploited with self-supervised contrastive learning for image hashing. However, the existing approaches suffer due to lack of effective utilization of global features by CNNs and biased-ness created by false negative pairs in the contrastive learning. In this paper, we propose a TransClippedCLR model by encoding the global context of an image using Transformer"},"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":"2401.15362","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-27T09:39:11Z","cross_cats_sorted":[],"title_canon_sha256":"112eba1bfb715191d1c951465193649379a3e9ede58186b13074eb544caa40b6","abstract_canon_sha256":"81cd6f47a785230129459e495369b1bbd623fe1bc6d7e72c377f8e89c1d3ff42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:15.090442Z","signature_b64":"/pPvTF4AHNgsdlakKHJOB3QxlBjU0jiedXt8/FiKYCZB4BIGyYO5nCLZPkBmvJKzsr6IPjMxqieCdXCT4pHWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1aaf74890de9dd8a5ae068f77a74cdc000e270b231ac3049ab97e42cb183e261","last_reissued_at":"2026-07-05T07:38:15.089898Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:15.089898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayush Dubey, Satish Kumar Singh, Shiv Ram Dubey, Wei-Ta Chu","submitted_at":"2024-01-27T09:39:11Z","abstract_excerpt":"Unsupervised image retrieval aims to learn the important visual characteristics without any given level to retrieve the similar images for a given query image. The Convolutional Neural Network (CNN)-based approaches have been extensively exploited with self-supervised contrastive learning for image hashing. However, the existing approaches suffer due to lack of effective utilization of global features by CNNs and biased-ness created by false negative pairs in the contrastive learning. In this paper, we propose a TransClippedCLR model by encoding the global context of an image using Transformer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15362","kind":"arxiv","version":1},"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/2401.15362/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":"2401.15362","created_at":"2026-07-05T07:38:15.089965+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.15362v1","created_at":"2026-07-05T07:38:15.089965+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15362","created_at":"2026-07-05T07:38:15.089965+00:00"},{"alias_kind":"pith_short_12","alias_value":"DKXXJCIN5HOY","created_at":"2026-07-05T07:38:15.089965+00:00"},{"alias_kind":"pith_short_16","alias_value":"DKXXJCIN5HOYUWXA","created_at":"2026-07-05T07:38:15.089965+00:00"},{"alias_kind":"pith_short_8","alias_value":"DKXXJCIN","created_at":"2026-07-05T07:38:15.089965+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA","json":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA.json","graph_json":"https://pith.science/api/pith-number/DKXXJCIN5HOYUWXAND3XU5GNYA/graph.json","events_json":"https://pith.science/api/pith-number/DKXXJCIN5HOYUWXAND3XU5GNYA/events.json","paper":"https://pith.science/paper/DKXXJCIN"},"agent_actions":{"view_html":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA","download_json":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA.json","view_paper":"https://pith.science/paper/DKXXJCIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.15362&json=true","fetch_graph":"https://pith.science/api/pith-number/DKXXJCIN5HOYUWXAND3XU5GNYA/graph.json","fetch_events":"https://pith.science/api/pith-number/DKXXJCIN5HOYUWXAND3XU5GNYA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA/action/storage_attestation","attest_author":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA/action/author_attestation","sign_citation":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA/action/citation_signature","submit_replication":"https://pith.science/pith/DKXXJCIN5HOYUWXAND3XU5GNYA/action/replication_record"}},"created_at":"2026-07-05T07:38:15.089965+00:00","updated_at":"2026-07-05T07:38:15.089965+00:00"}