{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SNRHRPVMEIJCM26MWYW5V6FDNT","short_pith_number":"pith:SNRHRPVM","schema_version":"1.0","canonical_sha256":"936278beac2212266bccb62ddaf8a36cdb40574bb47449e9e5524954cef76767","source":{"kind":"arxiv","id":"2208.09849","version":2},"attestation_state":"computed","paper":{"title":"Semantic-Enhanced Image Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Liping Qiu, Longteng Chen, Qin Zhang, Shaotian Cai, Xiaojun Chen","submitted_at":"2022-08-21T09:04:21Z","abstract_excerpt":"Image clustering is an important and open-challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images"},"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":"2208.09849","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-21T09:04:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9c64af7757763142458d2c70f25b2bd0b92a5aabe218429154306d125c6eace3","abstract_canon_sha256":"b0b42c9a5d05f423330a4ad8ea28a744e9b0fb094ed7b8dc666f423b0a1e3849"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:59:03.834056Z","signature_b64":"pnWO+UDmO98ig1pxp+BBDHEkuJFOi3ck6k3X3jVHuTz6oRBhkFpS0Md8la0mjMQKpbbQVWJuGlOO6t9GZp/5DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"936278beac2212266bccb62ddaf8a36cdb40574bb47449e9e5524954cef76767","last_reissued_at":"2026-07-05T05:59:03.833629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:59:03.833629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic-Enhanced Image Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Liping Qiu, Longteng Chen, Qin Zhang, Shaotian Cai, Xiaojun Chen","submitted_at":"2022-08-21T09:04:21Z","abstract_excerpt":"Image clustering is an important and open-challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09849","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/2208.09849/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":"2208.09849","created_at":"2026-07-05T05:59:03.833694+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09849v2","created_at":"2026-07-05T05:59:03.833694+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09849","created_at":"2026-07-05T05:59:03.833694+00:00"},{"alias_kind":"pith_short_12","alias_value":"SNRHRPVMEIJC","created_at":"2026-07-05T05:59:03.833694+00:00"},{"alias_kind":"pith_short_16","alias_value":"SNRHRPVMEIJCM26M","created_at":"2026-07-05T05:59:03.833694+00:00"},{"alias_kind":"pith_short_8","alias_value":"SNRHRPVM","created_at":"2026-07-05T05:59:03.833694+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.06814","citing_title":"Metadata Management for AI-Augmented Data Workflows","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT","json":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT.json","graph_json":"https://pith.science/api/pith-number/SNRHRPVMEIJCM26MWYW5V6FDNT/graph.json","events_json":"https://pith.science/api/pith-number/SNRHRPVMEIJCM26MWYW5V6FDNT/events.json","paper":"https://pith.science/paper/SNRHRPVM"},"agent_actions":{"view_html":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT","download_json":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT.json","view_paper":"https://pith.science/paper/SNRHRPVM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09849&json=true","fetch_graph":"https://pith.science/api/pith-number/SNRHRPVMEIJCM26MWYW5V6FDNT/graph.json","fetch_events":"https://pith.science/api/pith-number/SNRHRPVMEIJCM26MWYW5V6FDNT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT/action/storage_attestation","attest_author":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT/action/author_attestation","sign_citation":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT/action/citation_signature","submit_replication":"https://pith.science/pith/SNRHRPVMEIJCM26MWYW5V6FDNT/action/replication_record"}},"created_at":"2026-07-05T05:59:03.833694+00:00","updated_at":"2026-07-05T05:59:03.833694+00:00"}