{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:SL3SACPNSLRRTOA6P5DRWG7VB2","short_pith_number":"pith:SL3SACPN","canonical_record":{"source":{"id":"1908.06537","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-18T23:23:54Z","cross_cats_sorted":[],"title_canon_sha256":"fa19cc395b48f6bd20d99f024d6ef0d8372c00680a7b0d5c8d3db4f687f875ce","abstract_canon_sha256":"2c8c46e41c4d87ded26f6b29309670079b8e6d1e45135c12679cada3d3f3d5a7"},"schema_version":"1.0"},"canonical_sha256":"92f72009ed92e319b81e7f471b1bf50ea3bc746581b28b52b39442872603501b","source":{"kind":"arxiv","id":"1908.06537","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06537","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06537v1","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06537","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"pith_short_12","alias_value":"SL3SACPNSLRR","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"pith_short_16","alias_value":"SL3SACPNSLRRTOA6","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"pith_short_8","alias_value":"SL3SACPN","created_at":"2026-07-04T23:58:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:SL3SACPNSLRRTOA6P5DRWG7VB2","target":"record","payload":{"canonical_record":{"source":{"id":"1908.06537","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-18T23:23:54Z","cross_cats_sorted":[],"title_canon_sha256":"fa19cc395b48f6bd20d99f024d6ef0d8372c00680a7b0d5c8d3db4f687f875ce","abstract_canon_sha256":"2c8c46e41c4d87ded26f6b29309670079b8e6d1e45135c12679cada3d3f3d5a7"},"schema_version":"1.0"},"canonical_sha256":"92f72009ed92e319b81e7f471b1bf50ea3bc746581b28b52b39442872603501b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:16.923022Z","signature_b64":"z+jdKL7xSSyfxinlUMXyaBl+0ksO2U/VmbalhTFviyqg2y2KmjQNzGK0TvqkQu6/RvBpYuhvEnzQ7XV9EVgEDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92f72009ed92e319b81e7f471b1bf50ea3bc746581b28b52b39442872603501b","last_reissued_at":"2026-07-04T23:58:16.922689Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:16.922689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.06537","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":"2026-07-04T23:58:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rLfnbbFFwZIWKMwt9g3WG1L5GAHZ6b01UdRlAlBSwK2sOBFBoIMvOijC8I+xmtc6o2bmyyLz7pTV3cmCo81lCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T04:45:56.564782Z"},"content_sha256":"4093d63ed5acf528e44adffe4e1995e47f089d161ac8e2147f9e42ea03b2532b","schema_version":"1.0","event_id":"sha256:4093d63ed5acf528e44adffe4e1995e47f089d161ac8e2147f9e42ea03b2532b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:SL3SACPNSLRRTOA6P5DRWG7VB2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jean Ponce, Jongmin Lee, Juhong Min, Minsu Cho","submitted_at":"2019-08-18T23:23:54Z","abstract_excerpt":"Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details. To tackle these challenges, we represent images by \"hyperpixels\" that leverage a small number of relevant features selected among early to late layers of a convolutional neural network. Taking advantage of the condensed features of hyperpixels, we develop an effective real-time matching algorithm based on Hough geometric voting. The proposed method, hyperpixel fl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06537","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.06537/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-04T23:58:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hyVXr+bBqckmTM2rMDog9S1ZQtc+AMTokU9B5bN1oejRsAAwNRhoK/3kPo7wjPYz7zHt+DFP7U/KwmnVG3yhDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T04:45:56.565599Z"},"content_sha256":"d5f1ecdff89765ddbcf50f1f95da2cf271d495dd1f700ca09672015621871457","schema_version":"1.0","event_id":"sha256:d5f1ecdff89765ddbcf50f1f95da2cf271d495dd1f700ca09672015621871457"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SL3SACPNSLRRTOA6P5DRWG7VB2/bundle.json","state_url":"https://pith.science/pith/SL3SACPNSLRRTOA6P5DRWG7VB2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SL3SACPNSLRRTOA6P5DRWG7VB2/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":"2026-08-16T04:45:56Z","links":{"resolver":"https://pith.science/pith/SL3SACPNSLRRTOA6P5DRWG7VB2","bundle":"https://pith.science/pith/SL3SACPNSLRRTOA6P5DRWG7VB2/bundle.json","state":"https://pith.science/pith/SL3SACPNSLRRTOA6P5DRWG7VB2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SL3SACPNSLRRTOA6P5DRWG7VB2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SL3SACPNSLRRTOA6P5DRWG7VB2","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":"2c8c46e41c4d87ded26f6b29309670079b8e6d1e45135c12679cada3d3f3d5a7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-18T23:23:54Z","title_canon_sha256":"fa19cc395b48f6bd20d99f024d6ef0d8372c00680a7b0d5c8d3db4f687f875ce"},"schema_version":"1.0","source":{"id":"1908.06537","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06537","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06537v1","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06537","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"pith_short_12","alias_value":"SL3SACPNSLRR","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"pith_short_16","alias_value":"SL3SACPNSLRRTOA6","created_at":"2026-07-04T23:58:16Z"},{"alias_kind":"pith_short_8","alias_value":"SL3SACPN","created_at":"2026-07-04T23:58:16Z"}],"graph_snapshots":[{"event_id":"sha256:d5f1ecdff89765ddbcf50f1f95da2cf271d495dd1f700ca09672015621871457","target":"graph","created_at":"2026-07-04T23:58:16Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.06537/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details. To tackle these challenges, we represent images by \"hyperpixels\" that leverage a small number of relevant features selected among early to late layers of a convolutional neural network. Taking advantage of the condensed features of hyperpixels, we develop an effective real-time matching algorithm based on Hough geometric voting. The proposed method, hyperpixel fl","authors_text":"Jean Ponce, Jongmin Lee, Juhong Min, Minsu Cho","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-18T23:23:54Z","title":"Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06537","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":"sha256:4093d63ed5acf528e44adffe4e1995e47f089d161ac8e2147f9e42ea03b2532b","target":"record","created_at":"2026-07-04T23:58:16Z","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":"2c8c46e41c4d87ded26f6b29309670079b8e6d1e45135c12679cada3d3f3d5a7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-18T23:23:54Z","title_canon_sha256":"fa19cc395b48f6bd20d99f024d6ef0d8372c00680a7b0d5c8d3db4f687f875ce"},"schema_version":"1.0","source":{"id":"1908.06537","kind":"arxiv","version":1}},"canonical_sha256":"92f72009ed92e319b81e7f471b1bf50ea3bc746581b28b52b39442872603501b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"92f72009ed92e319b81e7f471b1bf50ea3bc746581b28b52b39442872603501b","first_computed_at":"2026-07-04T23:58:16.922689Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:58:16.922689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z+jdKL7xSSyfxinlUMXyaBl+0ksO2U/VmbalhTFviyqg2y2KmjQNzGK0TvqkQu6/RvBpYuhvEnzQ7XV9EVgEDQ==","signature_status":"signed_v1","signed_at":"2026-07-04T23:58:16.923022Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.06537","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4093d63ed5acf528e44adffe4e1995e47f089d161ac8e2147f9e42ea03b2532b","sha256:d5f1ecdff89765ddbcf50f1f95da2cf271d495dd1f700ca09672015621871457"],"state_sha256":"e04415cc4c5ef4df6be5eb69eff6fe40fbae2d7bfec6d0352eac81321c1cfce9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HxQmD2v/2TW8Nmnaw3QFDbcSYrJWaHT+FFvAou06LPxeU0Wp+7Go5tQHqkIVds9PdR2+uel7glHVFd+R913LBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T04:45:56.574232Z","bundle_sha256":"114cc8039e26db198647eee9df33da7464c97ce4a2af260760cf10843f31739d"}}