{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:3MTHU7Q77N3W2TKSIAFSVZQWDR","short_pith_number":"pith:3MTHU7Q7","canonical_record":{"source":{"id":"1809.05884","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T14:35:03Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM"],"title_canon_sha256":"21f71f0003eff56ba153e506f693a1a6bfb19493c2dd32e6b0084b36c6332ffa","abstract_canon_sha256":"6eb099d40106cf6b0d185f1a9c7f5306bed2e9ca0b8030004b46c80e3e2921ec"},"schema_version":"1.0"},"canonical_sha256":"db267a7e1ffb776d4d52400b2ae6161c4a95af48fa2393cfaeaba9fe9380f9f2","source":{"kind":"arxiv","id":"1809.05884","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.05884","created_at":"2026-05-17T23:53:02Z"},{"alias_kind":"arxiv_version","alias_value":"1809.05884v2","created_at":"2026-05-17T23:53:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05884","created_at":"2026-05-17T23:53:02Z"},{"alias_kind":"pith_short_12","alias_value":"3MTHU7Q77N3W","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"3MTHU7Q77N3W2TKS","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"3MTHU7Q7","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:3MTHU7Q77N3W2TKSIAFSVZQWDR","target":"record","payload":{"canonical_record":{"source":{"id":"1809.05884","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T14:35:03Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM"],"title_canon_sha256":"21f71f0003eff56ba153e506f693a1a6bfb19493c2dd32e6b0084b36c6332ffa","abstract_canon_sha256":"6eb099d40106cf6b0d185f1a9c7f5306bed2e9ca0b8030004b46c80e3e2921ec"},"schema_version":"1.0"},"canonical_sha256":"db267a7e1ffb776d4d52400b2ae6161c4a95af48fa2393cfaeaba9fe9380f9f2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:02.717296Z","signature_b64":"CfjsS1aX+eZk95xKl+9NR7S1b28W6k9EAj125xhH37euB3taKnTbr4ItfPjfswVU6UoOwhCNF20YQhwq1n1LCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db267a7e1ffb776d4d52400b2ae6161c4a95af48fa2393cfaeaba9fe9380f9f2","last_reissued_at":"2026-05-17T23:53:02.716646Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:02.716646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.05884","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:53:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FGLERXSx9NK8zKgDXFDRv8S0T7Ms25BGMy2LSbq6MYewyJ3aE5sLOIUh17nPwSJ8nHfui54n4Y70IVF+c5HEAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T20:49:31.034222Z"},"content_sha256":"1ff781d730ceba57332d317eedc36b3f87fb17ae6a5d25c2c9edefa29f29cf37","schema_version":"1.0","event_id":"sha256:1ff781d730ceba57332d317eedc36b3f87fb17ae6a5d25c2c9edefa29f29cf37"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:3MTHU7Q77N3W2TKSIAFSVZQWDR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Chunhong Pan, Jing Shao, Junjie Yan, Lu Sheng, Shiming Xiang, Yongcheng Liu","submitted_at":"2018-09-16T14:35:03Z","abstract_excerpt":"Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05884","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":""},"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":"2026-05-17T23:53:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IXyabdQ62Zssc2YEZW7X+RChGonaWyagouYsLZIjkTm+J0p0nxILjsy7jaD3ZhN9NdZEkfXdE7ejSsNvhPhPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T20:49:31.034996Z"},"content_sha256":"0910cdc3fe9cd010a89fe73372a481e5352098cc527be481b262a76b1c5e4af2","schema_version":"1.0","event_id":"sha256:0910cdc3fe9cd010a89fe73372a481e5352098cc527be481b262a76b1c5e4af2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR/bundle.json","state_url":"https://pith.science/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR/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":"2026-05-24T20:49:31Z","links":{"resolver":"https://pith.science/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR","bundle":"https://pith.science/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR/bundle.json","state":"https://pith.science/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3MTHU7Q77N3W2TKSIAFSVZQWDR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3MTHU7Q77N3W2TKSIAFSVZQWDR","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":"6eb099d40106cf6b0d185f1a9c7f5306bed2e9ca0b8030004b46c80e3e2921ec","cross_cats_sorted":["cs.AI","cs.LG","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T14:35:03Z","title_canon_sha256":"21f71f0003eff56ba153e506f693a1a6bfb19493c2dd32e6b0084b36c6332ffa"},"schema_version":"1.0","source":{"id":"1809.05884","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.05884","created_at":"2026-05-17T23:53:02Z"},{"alias_kind":"arxiv_version","alias_value":"1809.05884v2","created_at":"2026-05-17T23:53:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05884","created_at":"2026-05-17T23:53:02Z"},{"alias_kind":"pith_short_12","alias_value":"3MTHU7Q77N3W","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"3MTHU7Q77N3W2TKS","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"3MTHU7Q7","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:0910cdc3fe9cd010a89fe73372a481e5352098cc527be481b262a76b1c5e4af2","target":"graph","created_at":"2026-05-17T23:53:02Z","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"},"paper":{"abstract_excerpt":"Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Sp","authors_text":"Chunhong Pan, Jing Shao, Junjie Yan, Lu Sheng, Shiming Xiang, Yongcheng Liu","cross_cats":["cs.AI","cs.LG","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T14:35:03Z","title":"Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05884","kind":"arxiv","version":2},"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":"sha256:1ff781d730ceba57332d317eedc36b3f87fb17ae6a5d25c2c9edefa29f29cf37","target":"record","created_at":"2026-05-17T23:53:02Z","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":"6eb099d40106cf6b0d185f1a9c7f5306bed2e9ca0b8030004b46c80e3e2921ec","cross_cats_sorted":["cs.AI","cs.LG","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-16T14:35:03Z","title_canon_sha256":"21f71f0003eff56ba153e506f693a1a6bfb19493c2dd32e6b0084b36c6332ffa"},"schema_version":"1.0","source":{"id":"1809.05884","kind":"arxiv","version":2}},"canonical_sha256":"db267a7e1ffb776d4d52400b2ae6161c4a95af48fa2393cfaeaba9fe9380f9f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db267a7e1ffb776d4d52400b2ae6161c4a95af48fa2393cfaeaba9fe9380f9f2","first_computed_at":"2026-05-17T23:53:02.716646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:53:02.716646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CfjsS1aX+eZk95xKl+9NR7S1b28W6k9EAj125xhH37euB3taKnTbr4ItfPjfswVU6UoOwhCNF20YQhwq1n1LCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:53:02.717296Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.05884","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ff781d730ceba57332d317eedc36b3f87fb17ae6a5d25c2c9edefa29f29cf37","sha256:0910cdc3fe9cd010a89fe73372a481e5352098cc527be481b262a76b1c5e4af2"],"state_sha256":"9c183febc70490341e4428791b8ff7aa5476811f67f1b45f6d7afd68352df2ad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dy1FIucCQAfC5L1HNH09186qdsrjXbaa7Bt6+4NoKXuUTvDKoGRj6Fk0sY+CXFuppMJYBQLe9123E196Mt3lDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T20:49:31.039395Z","bundle_sha256":"d22036b29d8fc4df14d76a5533038ec49f150361feab3d1444860cf6b9c7fe91"}}