{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:67PU2MJWFY55SILA43SIJCUZWQ","short_pith_number":"pith:67PU2MJW","canonical_record":{"source":{"id":"1706.00906","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-03T07:37:59Z","cross_cats_sorted":[],"title_canon_sha256":"1847be091b8b2021c0798eefce57f83067553ce6b7295d52727771b22a6c0e51","abstract_canon_sha256":"e2eefc8a5e91cfe6cc159cec7a905176d4eb195b709f249d56c2d816e47a3c3f"},"schema_version":"1.0"},"canonical_sha256":"f7df4d31362e3bd92160e6e4848a99b43ad30c8e9e8858a0a611de8f880a3a92","source":{"kind":"arxiv","id":"1706.00906","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00906","created_at":"2026-05-18T00:34:09Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00906v3","created_at":"2026-05-18T00:34:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00906","created_at":"2026-05-18T00:34:09Z"},{"alias_kind":"pith_short_12","alias_value":"67PU2MJWFY55","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"67PU2MJWFY55SILA","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"67PU2MJW","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:67PU2MJWFY55SILA43SIJCUZWQ","target":"record","payload":{"canonical_record":{"source":{"id":"1706.00906","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-03T07:37:59Z","cross_cats_sorted":[],"title_canon_sha256":"1847be091b8b2021c0798eefce57f83067553ce6b7295d52727771b22a6c0e51","abstract_canon_sha256":"e2eefc8a5e91cfe6cc159cec7a905176d4eb195b709f249d56c2d816e47a3c3f"},"schema_version":"1.0"},"canonical_sha256":"f7df4d31362e3bd92160e6e4848a99b43ad30c8e9e8858a0a611de8f880a3a92","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:09.805002Z","signature_b64":"aqHGdRp+DIi0NKSTMI3lRQXHJLIf1Q2bt73AQQ5TUAn1PX54oS3y6Hk6j7GjdYmGLu8mZpSsWBOQ1ZKIOfFOBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7df4d31362e3bd92160e6e4848a99b43ad30c8e9e8858a0a611de8f880a3a92","last_reissued_at":"2026-05-18T00:34:09.804130Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:09.804130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.00906","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:34:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IHSnONGfxrkv8v/ejB0+Oq2YyCRT9U0ap/A25/uxCA/cwZISE+1ypNGx7+flo83Fbgkc1nWrU9P9FNJufkj7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T00:48:47.920502Z"},"content_sha256":"b7e0db472b9abb4dcc2c37b2c6ed624487033f1a78bbd8ce9edf4fcf01973e19","schema_version":"1.0","event_id":"sha256:b7e0db472b9abb4dcc2c37b2c6ed624487033f1a78bbd8ce9edf4fcf01973e19"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:67PU2MJWFY55SILA43SIJCUZWQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil K. Jain, Fang Wang, Hu Han, Shiguang Shan, Xilin Chen","submitted_at":"2017-06-03T07:37:59Z","abstract_excerpt":"Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00906","kind":"arxiv","version":3},"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":"2026-05-18T00:34:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2TTK5oalMetwCZROLYB2ESi/p/1Cf7aGFHe1PcauQLMrZbHFiMVrz8CRpF0gbtZYOb7l/O+dsQiCMpzlYuLCCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T00:48:47.920956Z"},"content_sha256":"d126d29cd7059add06d47ea5fe8843b2068335fb9cf6649b6a5f8d0cc2b61e19","schema_version":"1.0","event_id":"sha256:d126d29cd7059add06d47ea5fe8843b2068335fb9cf6649b6a5f8d0cc2b61e19"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/67PU2MJWFY55SILA43SIJCUZWQ/bundle.json","state_url":"https://pith.science/pith/67PU2MJWFY55SILA43SIJCUZWQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/67PU2MJWFY55SILA43SIJCUZWQ/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":"2026-05-24T00:48:47Z","links":{"resolver":"https://pith.science/pith/67PU2MJWFY55SILA43SIJCUZWQ","bundle":"https://pith.science/pith/67PU2MJWFY55SILA43SIJCUZWQ/bundle.json","state":"https://pith.science/pith/67PU2MJWFY55SILA43SIJCUZWQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/67PU2MJWFY55SILA43SIJCUZWQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:67PU2MJWFY55SILA43SIJCUZWQ","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":"e2eefc8a5e91cfe6cc159cec7a905176d4eb195b709f249d56c2d816e47a3c3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-03T07:37:59Z","title_canon_sha256":"1847be091b8b2021c0798eefce57f83067553ce6b7295d52727771b22a6c0e51"},"schema_version":"1.0","source":{"id":"1706.00906","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00906","created_at":"2026-05-18T00:34:09Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00906v3","created_at":"2026-05-18T00:34:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00906","created_at":"2026-05-18T00:34:09Z"},{"alias_kind":"pith_short_12","alias_value":"67PU2MJWFY55","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"67PU2MJWFY55SILA","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"67PU2MJW","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:d126d29cd7059add06d47ea5fe8843b2068335fb9cf6649b6a5f8d0cc2b61e19","target":"graph","created_at":"2026-05-18T00:34:09Z","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":"Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural","authors_text":"Anil K. Jain, Fang Wang, Hu Han, Shiguang Shan, Xilin Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-03T07:37:59Z","title":"Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00906","kind":"arxiv","version":3},"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:b7e0db472b9abb4dcc2c37b2c6ed624487033f1a78bbd8ce9edf4fcf01973e19","target":"record","created_at":"2026-05-18T00:34:09Z","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":"e2eefc8a5e91cfe6cc159cec7a905176d4eb195b709f249d56c2d816e47a3c3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-03T07:37:59Z","title_canon_sha256":"1847be091b8b2021c0798eefce57f83067553ce6b7295d52727771b22a6c0e51"},"schema_version":"1.0","source":{"id":"1706.00906","kind":"arxiv","version":3}},"canonical_sha256":"f7df4d31362e3bd92160e6e4848a99b43ad30c8e9e8858a0a611de8f880a3a92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7df4d31362e3bd92160e6e4848a99b43ad30c8e9e8858a0a611de8f880a3a92","first_computed_at":"2026-05-18T00:34:09.804130Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:09.804130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aqHGdRp+DIi0NKSTMI3lRQXHJLIf1Q2bt73AQQ5TUAn1PX54oS3y6Hk6j7GjdYmGLu8mZpSsWBOQ1ZKIOfFOBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:09.805002Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.00906","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7e0db472b9abb4dcc2c37b2c6ed624487033f1a78bbd8ce9edf4fcf01973e19","sha256:d126d29cd7059add06d47ea5fe8843b2068335fb9cf6649b6a5f8d0cc2b61e19"],"state_sha256":"eda1c439c0ee5924cdd8dbe8974f8baf84500aab261bf4433a8f0dea49943a55"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TyfOB67gwO1ou+FfKrdOmFzJyx1ijqNVe6Wa5ZpOQgKmC3Pv5dJ0eiHjbAJDiCXR79NXZBv+IPyeTiNg2O4pDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T00:48:47.924548Z","bundle_sha256":"148a64737873ba4f346e933861176f3cd48e5bc8614044e12fa6458959d16aaf"}}