{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:HGU4HB2MB33WWLT2VZYTQMJFVM","short_pith_number":"pith:HGU4HB2M","canonical_record":{"source":{"id":"1607.04780","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-16T18:14:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"03828f45a4fd3fe38ee197f46be726e896fd2ce326ba244394a3747e7c640257","abstract_canon_sha256":"c0f336fcd2a08e72a40d68eb28c6b675f8c1fe65e990ae01b917bee9881487cf"},"schema_version":"1.0"},"canonical_sha256":"39a9c3874c0ef76b2e7aae71383125ab0a1f671217d1654d749614b8bc88aca3","source":{"kind":"arxiv","id":"1607.04780","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.04780","created_at":"2026-05-18T01:10:56Z"},{"alias_kind":"arxiv_version","alias_value":"1607.04780v1","created_at":"2026-05-18T01:10:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.04780","created_at":"2026-05-18T01:10:56Z"},{"alias_kind":"pith_short_12","alias_value":"HGU4HB2MB33W","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HGU4HB2MB33WWLT2","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HGU4HB2M","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:HGU4HB2MB33WWLT2VZYTQMJFVM","target":"record","payload":{"canonical_record":{"source":{"id":"1607.04780","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-16T18:14:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"03828f45a4fd3fe38ee197f46be726e896fd2ce326ba244394a3747e7c640257","abstract_canon_sha256":"c0f336fcd2a08e72a40d68eb28c6b675f8c1fe65e990ae01b917bee9881487cf"},"schema_version":"1.0"},"canonical_sha256":"39a9c3874c0ef76b2e7aae71383125ab0a1f671217d1654d749614b8bc88aca3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:56.689288Z","signature_b64":"KO15hvBqihjqw5IB3nqz/tIycraaBt9L9d6RpouLO5GHVigsDL6LxcDPyQvlYsF5u4dl4tY+xtw5GsUdbM9vDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39a9c3874c0ef76b2e7aae71383125ab0a1f671217d1654d749614b8bc88aca3","last_reissued_at":"2026-05-18T01:10:56.688655Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:56.688655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.04780","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":"2026-05-18T01:10:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h9LND7qFcw3MSyd6PyINmZ+mLeJegw/ihIIbRWQ/8cYcxbH/Se92Q9kCLgDnAWqZ6WZ5pBw5R11Sj8vX4WwSDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:34:19.759472Z"},"content_sha256":"0d798ef24f50dcc334090a1070c99abc0471181d63a1762e046c6ef82e500dbd","schema_version":"1.0","event_id":"sha256:0d798ef24f50dcc334090a1070c99abc0471181d63a1762e046c6ef82e500dbd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:HGU4HB2MB33WWLT2VZYTQMJFVM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander Hauptmann, Deyu Meng, Junwei Liang, Lu Jiang","submitted_at":"2016-07-16T18:14:51Z","abstract_excerpt":"Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community. A considerable amount of videos on the web are associated with rich but noisy contextual information, such as the title, which provides weak annotations or labels about the video content. To leverage the big noisy web labels, this paper proposes a novel method called WEbly-Labeled Learning (WELL), which is established on the state-of-the-art machine learning algorithm inspired by the learning p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.04780","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":""},"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":"2026-05-18T01:10:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XQWIRrfBtXOoU/UVr8iue89f92fn7a9ercW3HDZ/hXttHOf8ZuhyS0YWQiiO7RRgrp/gv/k/Ik+pjpdLkH5ADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:34:19.760131Z"},"content_sha256":"2377177e8ddb85f36c3deb45411372f86b0dde1ef12a58238e1d3c02addfe23f","schema_version":"1.0","event_id":"sha256:2377177e8ddb85f36c3deb45411372f86b0dde1ef12a58238e1d3c02addfe23f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HGU4HB2MB33WWLT2VZYTQMJFVM/bundle.json","state_url":"https://pith.science/pith/HGU4HB2MB33WWLT2VZYTQMJFVM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HGU4HB2MB33WWLT2VZYTQMJFVM/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":"2026-05-25T07:34:19Z","links":{"resolver":"https://pith.science/pith/HGU4HB2MB33WWLT2VZYTQMJFVM","bundle":"https://pith.science/pith/HGU4HB2MB33WWLT2VZYTQMJFVM/bundle.json","state":"https://pith.science/pith/HGU4HB2MB33WWLT2VZYTQMJFVM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HGU4HB2MB33WWLT2VZYTQMJFVM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:HGU4HB2MB33WWLT2VZYTQMJFVM","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":"c0f336fcd2a08e72a40d68eb28c6b675f8c1fe65e990ae01b917bee9881487cf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-16T18:14:51Z","title_canon_sha256":"03828f45a4fd3fe38ee197f46be726e896fd2ce326ba244394a3747e7c640257"},"schema_version":"1.0","source":{"id":"1607.04780","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.04780","created_at":"2026-05-18T01:10:56Z"},{"alias_kind":"arxiv_version","alias_value":"1607.04780v1","created_at":"2026-05-18T01:10:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.04780","created_at":"2026-05-18T01:10:56Z"},{"alias_kind":"pith_short_12","alias_value":"HGU4HB2MB33W","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HGU4HB2MB33WWLT2","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HGU4HB2M","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:2377177e8ddb85f36c3deb45411372f86b0dde1ef12a58238e1d3c02addfe23f","target":"graph","created_at":"2026-05-18T01:10:56Z","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":"Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community. A considerable amount of videos on the web are associated with rich but noisy contextual information, such as the title, which provides weak annotations or labels about the video content. To leverage the big noisy web labels, this paper proposes a novel method called WEbly-Labeled Learning (WELL), which is established on the state-of-the-art machine learning algorithm inspired by the learning p","authors_text":"Alexander Hauptmann, Deyu Meng, Junwei Liang, Lu Jiang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-16T18:14:51Z","title":"Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.04780","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":"sha256:0d798ef24f50dcc334090a1070c99abc0471181d63a1762e046c6ef82e500dbd","target":"record","created_at":"2026-05-18T01:10:56Z","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":"c0f336fcd2a08e72a40d68eb28c6b675f8c1fe65e990ae01b917bee9881487cf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-16T18:14:51Z","title_canon_sha256":"03828f45a4fd3fe38ee197f46be726e896fd2ce326ba244394a3747e7c640257"},"schema_version":"1.0","source":{"id":"1607.04780","kind":"arxiv","version":1}},"canonical_sha256":"39a9c3874c0ef76b2e7aae71383125ab0a1f671217d1654d749614b8bc88aca3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39a9c3874c0ef76b2e7aae71383125ab0a1f671217d1654d749614b8bc88aca3","first_computed_at":"2026-05-18T01:10:56.688655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:10:56.688655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KO15hvBqihjqw5IB3nqz/tIycraaBt9L9d6RpouLO5GHVigsDL6LxcDPyQvlYsF5u4dl4tY+xtw5GsUdbM9vDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:10:56.689288Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.04780","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d798ef24f50dcc334090a1070c99abc0471181d63a1762e046c6ef82e500dbd","sha256:2377177e8ddb85f36c3deb45411372f86b0dde1ef12a58238e1d3c02addfe23f"],"state_sha256":"66a8b50733da9b33884e29504a818021b76b992ae2a78ea910c3c5266e812521"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GBJ4dPasmzxyLUGFnXNxsId52ViXQKUywlT4r8M/SQdIzU7LOayqPx4H7myD7QaP9cacScWmpaS+MtZzSwtmCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T07:34:19.763497Z","bundle_sha256":"2937cd8652c6253ad87dc5f64551a0e321fb8605d7dad9f9b0acf21601be51c3"}}