{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:4AKJNOKUIEP2SIQ7OW5EN57RGK","short_pith_number":"pith:4AKJNOKU","canonical_record":{"source":{"id":"1511.06881","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-21T13:32:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5869a47dcadef9ebb40df157322d1f13ef53d4ae11cbca696e451e33fecc8958","abstract_canon_sha256":"4e402bf474ae4626baa7f512f1f222c095ce892ff2e156de6a3083edb33412f2"},"schema_version":"1.0"},"canonical_sha256":"e01496b954411fa9221f75ba46f7f132b7edd2c1c25dd7138c38269c2fa647e6","source":{"kind":"arxiv","id":"1511.06881","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06881","created_at":"2026-05-18T01:18:08Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06881v5","created_at":"2026-05-18T01:18:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06881","created_at":"2026-05-18T01:18:08Z"},{"alias_kind":"pith_short_12","alias_value":"4AKJNOKUIEP2","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"4AKJNOKUIEP2SIQ7","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"4AKJNOKU","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:4AKJNOKUIEP2SIQ7OW5EN57RGK","target":"record","payload":{"canonical_record":{"source":{"id":"1511.06881","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-21T13:32:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5869a47dcadef9ebb40df157322d1f13ef53d4ae11cbca696e451e33fecc8958","abstract_canon_sha256":"4e402bf474ae4626baa7f512f1f222c095ce892ff2e156de6a3083edb33412f2"},"schema_version":"1.0"},"canonical_sha256":"e01496b954411fa9221f75ba46f7f132b7edd2c1c25dd7138c38269c2fa647e6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:08.990305Z","signature_b64":"jIMkO7kql7A6pqP0QVnBJFHGRozrXEYjCviffpUUbL/O1OKHftOl6CDxTFwW56h5kTtrCNJJ3xMGuR5C1k+HDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e01496b954411fa9221f75ba46f7f132b7edd2c1c25dd7138c38269c2fa647e6","last_reissued_at":"2026-05-18T01:18:08.989614Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:08.989614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.06881","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:18:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ebb/RfKk1xEiE9RI0rp8xdHtdQEmXJkMWfZ4gwXHugKuPcnMCy6vcnY8D06f8ijK6V2CJrmbEkdoKSt6+6vcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T08:49:53.306930Z"},"content_sha256":"726b8682fd31417a246b4c373b9e7f947c06dba159aac1cdc6ebdf803f2d53d8","schema_version":"1.0","event_id":"sha256:726b8682fd31417a246b4c373b9e7f947c06dba159aac1cdc6ebdf803f2d53d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:4AKJNOKUIEP2SIQ7OW5EN57RGK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alan L. Yuille, Fangting Xia, Liang-Chieh Chen, Peng Wang","submitted_at":"2015-11-21T13:32:26Z","abstract_excerpt":"Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a \"Hierarchical Auto-Zoom Net\" (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06881","kind":"arxiv","version":5},"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":"2026-05-18T01:18:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rISvPSDzon1hmWXOKKSF1w95hd39IUJ8wOvEHvVDVDFiIA/T6hR/nnd8gZ2p1bDlrReNCyozj8KbY6PN7xUyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T08:49:53.307391Z"},"content_sha256":"f1d8cb0758db6275ee65548b9de83e27d688221184c5aa603d956672e6b55e05","schema_version":"1.0","event_id":"sha256:f1d8cb0758db6275ee65548b9de83e27d688221184c5aa603d956672e6b55e05"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK/bundle.json","state_url":"https://pith.science/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK/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":"2026-08-22T08:49:53Z","links":{"resolver":"https://pith.science/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK","bundle":"https://pith.science/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK/bundle.json","state":"https://pith.science/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4AKJNOKUIEP2SIQ7OW5EN57RGK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:4AKJNOKUIEP2SIQ7OW5EN57RGK","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":"4e402bf474ae4626baa7f512f1f222c095ce892ff2e156de6a3083edb33412f2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-21T13:32:26Z","title_canon_sha256":"5869a47dcadef9ebb40df157322d1f13ef53d4ae11cbca696e451e33fecc8958"},"schema_version":"1.0","source":{"id":"1511.06881","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06881","created_at":"2026-05-18T01:18:08Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06881v5","created_at":"2026-05-18T01:18:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06881","created_at":"2026-05-18T01:18:08Z"},{"alias_kind":"pith_short_12","alias_value":"4AKJNOKUIEP2","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"4AKJNOKUIEP2SIQ7","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"4AKJNOKU","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:f1d8cb0758db6275ee65548b9de83e27d688221184c5aa603d956672e6b55e05","target":"graph","created_at":"2026-05-18T01:18:08Z","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":"Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a \"Hierarchical Auto-Zoom Net\" (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of","authors_text":"Alan L. Yuille, Fangting Xia, Liang-Chieh Chen, Peng Wang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-21T13:32:26Z","title":"Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06881","kind":"arxiv","version":5},"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":"sha256:726b8682fd31417a246b4c373b9e7f947c06dba159aac1cdc6ebdf803f2d53d8","target":"record","created_at":"2026-05-18T01:18:08Z","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":"4e402bf474ae4626baa7f512f1f222c095ce892ff2e156de6a3083edb33412f2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-21T13:32:26Z","title_canon_sha256":"5869a47dcadef9ebb40df157322d1f13ef53d4ae11cbca696e451e33fecc8958"},"schema_version":"1.0","source":{"id":"1511.06881","kind":"arxiv","version":5}},"canonical_sha256":"e01496b954411fa9221f75ba46f7f132b7edd2c1c25dd7138c38269c2fa647e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e01496b954411fa9221f75ba46f7f132b7edd2c1c25dd7138c38269c2fa647e6","first_computed_at":"2026-05-18T01:18:08.989614Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:18:08.989614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jIMkO7kql7A6pqP0QVnBJFHGRozrXEYjCviffpUUbL/O1OKHftOl6CDxTFwW56h5kTtrCNJJ3xMGuR5C1k+HDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:18:08.990305Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.06881","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:726b8682fd31417a246b4c373b9e7f947c06dba159aac1cdc6ebdf803f2d53d8","sha256:f1d8cb0758db6275ee65548b9de83e27d688221184c5aa603d956672e6b55e05"],"state_sha256":"42e2e94fba2ce7f8cefd706e0ea1aa8499440b656838836b975e72867e43e6c4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XB/81EwYwp853aGSr0RohTBiCxNaKMkqkY0QdkF2me9KpLXfIbeRvmnF4Ep+oH+6+FfrRlGopFOZSzKjE22/Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T08:49:53.310874Z","bundle_sha256":"493329a1dcc36f82b0ddf0dc4393b36513e53dd14a25f958aa07ee693bc09bb5"}}