{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:IDEHHQBOPD7QZATEAN6KYXNOIR","short_pith_number":"pith:IDEHHQBO","canonical_record":{"source":{"id":"2607.00839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T12:05:57Z","cross_cats_sorted":[],"title_canon_sha256":"3b6d8aa91dd599670f7deaea2b69c204a05fd7e24df2234daef1c9a4801da189","abstract_canon_sha256":"d9b771c49406d48a8deed4c95c53ddc365cf8c5b8920676bc10902f696f29e23"},"schema_version":"1.0"},"canonical_sha256":"40c873c02e78ff0c8264037cac5dae446265c5f1e5ed03d58b1b04b534666d51","source":{"kind":"arxiv","id":"2607.00839","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.00839","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"arxiv_version","alias_value":"2607.00839v1","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00839","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"pith_short_12","alias_value":"IDEHHQBOPD7Q","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"pith_short_16","alias_value":"IDEHHQBOPD7QZATE","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"pith_short_8","alias_value":"IDEHHQBO","created_at":"2026-07-02T01:18:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:IDEHHQBOPD7QZATEAN6KYXNOIR","target":"record","payload":{"canonical_record":{"source":{"id":"2607.00839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T12:05:57Z","cross_cats_sorted":[],"title_canon_sha256":"3b6d8aa91dd599670f7deaea2b69c204a05fd7e24df2234daef1c9a4801da189","abstract_canon_sha256":"d9b771c49406d48a8deed4c95c53ddc365cf8c5b8920676bc10902f696f29e23"},"schema_version":"1.0"},"canonical_sha256":"40c873c02e78ff0c8264037cac5dae446265c5f1e5ed03d58b1b04b534666d51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:18:21.181805Z","signature_b64":"wzW/uqc1oNcF0sgorFD4N6iKXgLCayLhtOwARHqDZ8GUY+6MQZXcoe5YesJVMhjo7SEGzPj1u0KSafsp13qrAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40c873c02e78ff0c8264037cac5dae446265c5f1e5ed03d58b1b04b534666d51","last_reissued_at":"2026-07-02T01:18:21.181356Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:18:21.181356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.00839","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-02T01:18:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QlXU42QtU4Z7CS0iyh6crXY8c7mV+6hht4HhuOWyMvw5hWjs1pxiZtKy9bQRN1DIDQmIf4bxDyHT0dTgJV/dDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T04:29:00.914931Z"},"content_sha256":"3220958a5a8b620d59de9eedb6793b0eeacdfd762b1087ad0fa8e3243c6a3524","schema_version":"1.0","event_id":"sha256:3220958a5a8b620d59de9eedb6793b0eeacdfd762b1087ad0fa8e3243c6a3524"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:IDEHHQBOPD7QZATEAN6KYXNOIR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Wang, Jiawei Ge, Shuai Xu, Xiu-Shen Wei, Xuelin Zhu","submitted_at":"2026-07-01T12:05:57Z","abstract_excerpt":"Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-world applications, such as autonomous driving, disease diagnosis, recommendation system, and mobile service robot. Over the past decade, deep learning paradigms based on convolutional neural networks, recurrent neural networks, and Transformers have significantly advanced this field, owing to their powerful capability in visual representation and relationship modeling. These advances have markedly improved the robustnes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00839","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/2607.00839/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-02T01:18:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UoJlmxkRNcTs78WfWs3RpnVHAjHY120wqHXgz9hla1e1aQZs+o93b8WcgsBOwhX2CdGvKUZfiZxW2z4wi3tLBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T04:29:00.915582Z"},"content_sha256":"0d36fc4db207a7c7c5f2e4d592d53527c7b11c9ee0d0769e1b6118517f7ec063","schema_version":"1.0","event_id":"sha256:0d36fc4db207a7c7c5f2e4d592d53527c7b11c9ee0d0769e1b6118517f7ec063"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IDEHHQBOPD7QZATEAN6KYXNOIR/bundle.json","state_url":"https://pith.science/pith/IDEHHQBOPD7QZATEAN6KYXNOIR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IDEHHQBOPD7QZATEAN6KYXNOIR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-01T04:29:00Z","links":{"resolver":"https://pith.science/pith/IDEHHQBOPD7QZATEAN6KYXNOIR","bundle":"https://pith.science/pith/IDEHHQBOPD7QZATEAN6KYXNOIR/bundle.json","state":"https://pith.science/pith/IDEHHQBOPD7QZATEAN6KYXNOIR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IDEHHQBOPD7QZATEAN6KYXNOIR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IDEHHQBOPD7QZATEAN6KYXNOIR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d9b771c49406d48a8deed4c95c53ddc365cf8c5b8920676bc10902f696f29e23","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T12:05:57Z","title_canon_sha256":"3b6d8aa91dd599670f7deaea2b69c204a05fd7e24df2234daef1c9a4801da189"},"schema_version":"1.0","source":{"id":"2607.00839","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.00839","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"arxiv_version","alias_value":"2607.00839v1","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00839","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"pith_short_12","alias_value":"IDEHHQBOPD7Q","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"pith_short_16","alias_value":"IDEHHQBOPD7QZATE","created_at":"2026-07-02T01:18:21Z"},{"alias_kind":"pith_short_8","alias_value":"IDEHHQBO","created_at":"2026-07-02T01:18:21Z"}],"graph_snapshots":[{"event_id":"sha256:0d36fc4db207a7c7c5f2e4d592d53527c7b11c9ee0d0769e1b6118517f7ec063","target":"graph","created_at":"2026-07-02T01:18:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.00839/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-world applications, such as autonomous driving, disease diagnosis, recommendation system, and mobile service robot. Over the past decade, deep learning paradigms based on convolutional neural networks, recurrent neural networks, and Transformers have significantly advanced this field, owing to their powerful capability in visual representation and relationship modeling. These advances have markedly improved the robustnes","authors_text":"Bing Wang, Jiawei Ge, Shuai Xu, Xiu-Shen Wei, Xuelin Zhu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T12:05:57Z","title":"Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00839","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3220958a5a8b620d59de9eedb6793b0eeacdfd762b1087ad0fa8e3243c6a3524","target":"record","created_at":"2026-07-02T01:18:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d9b771c49406d48a8deed4c95c53ddc365cf8c5b8920676bc10902f696f29e23","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T12:05:57Z","title_canon_sha256":"3b6d8aa91dd599670f7deaea2b69c204a05fd7e24df2234daef1c9a4801da189"},"schema_version":"1.0","source":{"id":"2607.00839","kind":"arxiv","version":1}},"canonical_sha256":"40c873c02e78ff0c8264037cac5dae446265c5f1e5ed03d58b1b04b534666d51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40c873c02e78ff0c8264037cac5dae446265c5f1e5ed03d58b1b04b534666d51","first_computed_at":"2026-07-02T01:18:21.181356Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T01:18:21.181356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wzW/uqc1oNcF0sgorFD4N6iKXgLCayLhtOwARHqDZ8GUY+6MQZXcoe5YesJVMhjo7SEGzPj1u0KSafsp13qrAg==","signature_status":"signed_v1","signed_at":"2026-07-02T01:18:21.181805Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.00839","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3220958a5a8b620d59de9eedb6793b0eeacdfd762b1087ad0fa8e3243c6a3524","sha256:0d36fc4db207a7c7c5f2e4d592d53527c7b11c9ee0d0769e1b6118517f7ec063"],"state_sha256":"65c1a248366a5c20015bc78a8dd6ff566aa5dd0dbea8ec689e29990cad858683"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MXoQ53wUxHyenT5bIv+AD+x3FC02rWODNgm1lmEbpicqzbLvZbzYZ8obco8l8clMmESgQ8oMpipLQBJLhVdkCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T04:29:00.928496Z","bundle_sha256":"6179d8928a9aa16b5dc5843775b9954b0f0c8e80204b2fe101186e5ad8260a90"}}