{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:S6LPKQEEITBDE6B2IFGV4V2VSI","short_pith_number":"pith:S6LPKQEE","canonical_record":{"source":{"id":"1807.03903","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-10T23:49:03Z","cross_cats_sorted":[],"title_canon_sha256":"609b6a655f5f25ea99eca99c9200d8ac0a8d1eb3daef0cc90c1eb502b34913ca","abstract_canon_sha256":"796e938233482aa23ec1251ffa6864f9e6bb937e17d7b81927b8a63fc26ec1a5"},"schema_version":"1.0"},"canonical_sha256":"9796f5408444c232783a414d5e5755922835921d03dc19fb5938c13952680bf2","source":{"kind":"arxiv","id":"1807.03903","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03903","created_at":"2026-05-18T00:09:46Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03903v2","created_at":"2026-05-18T00:09:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03903","created_at":"2026-05-18T00:09:46Z"},{"alias_kind":"pith_short_12","alias_value":"S6LPKQEEITBD","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"S6LPKQEEITBDE6B2","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"S6LPKQEE","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:S6LPKQEEITBDE6B2IFGV4V2VSI","target":"record","payload":{"canonical_record":{"source":{"id":"1807.03903","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-10T23:49:03Z","cross_cats_sorted":[],"title_canon_sha256":"609b6a655f5f25ea99eca99c9200d8ac0a8d1eb3daef0cc90c1eb502b34913ca","abstract_canon_sha256":"796e938233482aa23ec1251ffa6864f9e6bb937e17d7b81927b8a63fc26ec1a5"},"schema_version":"1.0"},"canonical_sha256":"9796f5408444c232783a414d5e5755922835921d03dc19fb5938c13952680bf2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:46.796799Z","signature_b64":"V55wkjfOr8k3orM3hbN4uG+a95wWdauXYoTKLN1tbxxl5nZAjdOdalCC83G0xHVoRBbUFClP/9PmuP0VmI7RBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9796f5408444c232783a414d5e5755922835921d03dc19fb5938c13952680bf2","last_reissued_at":"2026-05-18T00:09:46.796115Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:46.796115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.03903","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":"2026-05-18T00:09:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OVVgT8n0QoF/RIIDdrJttXO9OUHb4ED4bl/zZZmMLBTTZhAeS6pzWgV+qlCv6knIQeg+yfznRm+9/U72psFjCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:23:42.579412Z"},"content_sha256":"6922aade07130939658963250326be1a67de5dba1b3822bc5028ef08b2340426","schema_version":"1.0","event_id":"sha256:6922aade07130939658963250326be1a67de5dba1b3822bc5028ef08b2340426"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:S6LPKQEEITBDE6B2IFGV4V2VSI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Imbalanced Attribute Classification using Visual Attention Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ioannis A. Kakadiaris, Nikolaos Sarafianos, Xiang Xu","submitted_at":"2018-07-10T23:49:03Z","abstract_excerpt":"For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods follow either a computer vision approach while failing to account for class imbalance, or explore machine learning solutions, which disregard the spatial and semantic relations that exist in the images. With that in mind, we propose an effective method that extracts and aggregat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03903","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":"2026-05-18T00:09:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2iXuIOEngirtNb7chKoF9U+NVxaYswjeJLSW+yaRypNBW9wCY81eV8uljdx4pbg9aXoXiFZmyF6S//OOOzlBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:23:42.580119Z"},"content_sha256":"8a4d5850af016c98db40c80e8d2e66a98c97a127a6baf86325ac9332c715e71f","schema_version":"1.0","event_id":"sha256:8a4d5850af016c98db40c80e8d2e66a98c97a127a6baf86325ac9332c715e71f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S6LPKQEEITBDE6B2IFGV4V2VSI/bundle.json","state_url":"https://pith.science/pith/S6LPKQEEITBDE6B2IFGV4V2VSI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S6LPKQEEITBDE6B2IFGV4V2VSI/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":"2026-05-25T07:23:42Z","links":{"resolver":"https://pith.science/pith/S6LPKQEEITBDE6B2IFGV4V2VSI","bundle":"https://pith.science/pith/S6LPKQEEITBDE6B2IFGV4V2VSI/bundle.json","state":"https://pith.science/pith/S6LPKQEEITBDE6B2IFGV4V2VSI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S6LPKQEEITBDE6B2IFGV4V2VSI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:S6LPKQEEITBDE6B2IFGV4V2VSI","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":"796e938233482aa23ec1251ffa6864f9e6bb937e17d7b81927b8a63fc26ec1a5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-10T23:49:03Z","title_canon_sha256":"609b6a655f5f25ea99eca99c9200d8ac0a8d1eb3daef0cc90c1eb502b34913ca"},"schema_version":"1.0","source":{"id":"1807.03903","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03903","created_at":"2026-05-18T00:09:46Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03903v2","created_at":"2026-05-18T00:09:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03903","created_at":"2026-05-18T00:09:46Z"},{"alias_kind":"pith_short_12","alias_value":"S6LPKQEEITBD","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"S6LPKQEEITBDE6B2","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"S6LPKQEE","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:8a4d5850af016c98db40c80e8d2e66a98c97a127a6baf86325ac9332c715e71f","target":"graph","created_at":"2026-05-18T00:09:46Z","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":"For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods follow either a computer vision approach while failing to account for class imbalance, or explore machine learning solutions, which disregard the spatial and semantic relations that exist in the images. With that in mind, we propose an effective method that extracts and aggregat","authors_text":"Ioannis A. Kakadiaris, Nikolaos Sarafianos, Xiang Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-10T23:49:03Z","title":"Deep Imbalanced Attribute Classification using Visual Attention Aggregation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03903","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":"sha256:6922aade07130939658963250326be1a67de5dba1b3822bc5028ef08b2340426","target":"record","created_at":"2026-05-18T00:09:46Z","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":"796e938233482aa23ec1251ffa6864f9e6bb937e17d7b81927b8a63fc26ec1a5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-10T23:49:03Z","title_canon_sha256":"609b6a655f5f25ea99eca99c9200d8ac0a8d1eb3daef0cc90c1eb502b34913ca"},"schema_version":"1.0","source":{"id":"1807.03903","kind":"arxiv","version":2}},"canonical_sha256":"9796f5408444c232783a414d5e5755922835921d03dc19fb5938c13952680bf2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9796f5408444c232783a414d5e5755922835921d03dc19fb5938c13952680bf2","first_computed_at":"2026-05-18T00:09:46.796115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:46.796115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V55wkjfOr8k3orM3hbN4uG+a95wWdauXYoTKLN1tbxxl5nZAjdOdalCC83G0xHVoRBbUFClP/9PmuP0VmI7RBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:46.796799Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.03903","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6922aade07130939658963250326be1a67de5dba1b3822bc5028ef08b2340426","sha256:8a4d5850af016c98db40c80e8d2e66a98c97a127a6baf86325ac9332c715e71f"],"state_sha256":"f807ae677fbb296961b5e05756e2501ec8f2215bd5ded850b7318e5bd5a89c10"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xksD4KqJHUPQL+43ZzXzU5Gmisp3WDcuOtBmNq4bYCgVHyk+sGIpfSEwn61BapCE4nC3V7IjML72YbpVJn/NAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T07:23:42.583380Z","bundle_sha256":"6206706c7ada3e65af2cf262eefa711531587ca97026a8693bf5c176e0fe0c50"}}