{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:NHPDUTOIJTDTDT5WPOFNNY4J4C","short_pith_number":"pith:NHPDUTOI","canonical_record":{"source":{"id":"2012.12519","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-23T07:14:53Z","cross_cats_sorted":[],"title_canon_sha256":"b8fa9c37e24a707160c956af850165e3be50c132041c9525c83ba00c2eb4570f","abstract_canon_sha256":"5c813465f3aa8b41b7f6b4cf154d1b54c3bfbb161fb8105bd076bf2a4717d138"},"schema_version":"1.0"},"canonical_sha256":"69de3a4dc84cc731cfb67b8ad6e389e08ff171016a2fc172597840de1d962927","source":{"kind":"arxiv","id":"2012.12519","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.12519","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"arxiv_version","alias_value":"2012.12519v1","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.12519","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"pith_short_12","alias_value":"NHPDUTOIJTDT","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"pith_short_16","alias_value":"NHPDUTOIJTDTDT5W","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"pith_short_8","alias_value":"NHPDUTOI","created_at":"2026-07-05T02:01:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:NHPDUTOIJTDTDT5WPOFNNY4J4C","target":"record","payload":{"canonical_record":{"source":{"id":"2012.12519","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-23T07:14:53Z","cross_cats_sorted":[],"title_canon_sha256":"b8fa9c37e24a707160c956af850165e3be50c132041c9525c83ba00c2eb4570f","abstract_canon_sha256":"5c813465f3aa8b41b7f6b4cf154d1b54c3bfbb161fb8105bd076bf2a4717d138"},"schema_version":"1.0"},"canonical_sha256":"69de3a4dc84cc731cfb67b8ad6e389e08ff171016a2fc172597840de1d962927","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:01:37.012955Z","signature_b64":"6tDQ4wrjmtZWxIf38Ld4WgRFjSNCfPBIOlqjHRqY7zDgnhtKhhEWQhjK6PeHEDa6I22Rj+iM8ZRBoDKPBZL+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69de3a4dc84cc731cfb67b8ad6e389e08ff171016a2fc172597840de1d962927","last_reissued_at":"2026-07-05T02:01:37.012538Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:01:37.012538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.12519","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":"2026-07-05T02:01:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DgzY7iqrs3tyCinmKFXZS0scRSG7yBKcgcPxbVnq5VhspL9Vcvms3Jvy3W7phTJD37yr260sNlEJ7idVwwMUAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T23:01:30.421961Z"},"content_sha256":"57bd364bf517a0397f555d4c155310a6d763572a8138eae864ba6ffb71dcc914","schema_version":"1.0","event_id":"sha256:57bd364bf517a0397f555d4c155310a6d763572a8138eae864ba6ffb71dcc914"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:NHPDUTOIJTDTDT5WPOFNNY4J4C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vehicle Re-identification Based on Dual Distance Center Loss","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jie Wen, Lilei Sun, Raja S P, Yong Xu, Zhijun Hu","submitted_at":"2020-12-23T07:14:53Z","abstract_excerpt":"Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the softmax loss performs well for closed-set tasks, but not very well for open-set tasks. In this paper, we sum up five shortcomings of center loss and solved all of them by proposing a dual distance center loss (DDCL). Especially we solve the shortcoming that center loss must combine with the softmax loss to supervise training the model, which provides us with a new perspective to examine the center loss. In addition,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.12519","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/2012.12519/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":"2026-07-05T02:01:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JbN8ln0JYw3tJXqaW8L1in3W9zNnW5qtv9x71cPbXjRP/qtYafGG79sPxneMmH0doRo4imHHHFP6Z+VzNAeKBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T23:01:30.422636Z"},"content_sha256":"e5cc5b800ed00239ccac22334c605871c102e114c42e58a15cb3f695743a5599","schema_version":"1.0","event_id":"sha256:e5cc5b800ed00239ccac22334c605871c102e114c42e58a15cb3f695743a5599"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C/bundle.json","state_url":"https://pith.science/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C/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":"2026-08-06T23:01:30Z","links":{"resolver":"https://pith.science/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C","bundle":"https://pith.science/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C/bundle.json","state":"https://pith.science/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NHPDUTOIJTDTDT5WPOFNNY4J4C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NHPDUTOIJTDTDT5WPOFNNY4J4C","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":"5c813465f3aa8b41b7f6b4cf154d1b54c3bfbb161fb8105bd076bf2a4717d138","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-23T07:14:53Z","title_canon_sha256":"b8fa9c37e24a707160c956af850165e3be50c132041c9525c83ba00c2eb4570f"},"schema_version":"1.0","source":{"id":"2012.12519","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.12519","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"arxiv_version","alias_value":"2012.12519v1","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.12519","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"pith_short_12","alias_value":"NHPDUTOIJTDT","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"pith_short_16","alias_value":"NHPDUTOIJTDTDT5W","created_at":"2026-07-05T02:01:37Z"},{"alias_kind":"pith_short_8","alias_value":"NHPDUTOI","created_at":"2026-07-05T02:01:37Z"}],"graph_snapshots":[{"event_id":"sha256:e5cc5b800ed00239ccac22334c605871c102e114c42e58a15cb3f695743a5599","target":"graph","created_at":"2026-07-05T02:01:37Z","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/2012.12519/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the softmax loss performs well for closed-set tasks, but not very well for open-set tasks. In this paper, we sum up five shortcomings of center loss and solved all of them by proposing a dual distance center loss (DDCL). Especially we solve the shortcoming that center loss must combine with the softmax loss to supervise training the model, which provides us with a new perspective to examine the center loss. In addition,","authors_text":"Jie Wen, Lilei Sun, Raja S P, Yong Xu, Zhijun Hu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-23T07:14:53Z","title":"Vehicle Re-identification Based on Dual Distance Center Loss"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.12519","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":"sha256:57bd364bf517a0397f555d4c155310a6d763572a8138eae864ba6ffb71dcc914","target":"record","created_at":"2026-07-05T02:01:37Z","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":"5c813465f3aa8b41b7f6b4cf154d1b54c3bfbb161fb8105bd076bf2a4717d138","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-23T07:14:53Z","title_canon_sha256":"b8fa9c37e24a707160c956af850165e3be50c132041c9525c83ba00c2eb4570f"},"schema_version":"1.0","source":{"id":"2012.12519","kind":"arxiv","version":1}},"canonical_sha256":"69de3a4dc84cc731cfb67b8ad6e389e08ff171016a2fc172597840de1d962927","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"69de3a4dc84cc731cfb67b8ad6e389e08ff171016a2fc172597840de1d962927","first_computed_at":"2026-07-05T02:01:37.012538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:01:37.012538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6tDQ4wrjmtZWxIf38Ld4WgRFjSNCfPBIOlqjHRqY7zDgnhtKhhEWQhjK6PeHEDa6I22Rj+iM8ZRBoDKPBZL+Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:01:37.012955Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.12519","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57bd364bf517a0397f555d4c155310a6d763572a8138eae864ba6ffb71dcc914","sha256:e5cc5b800ed00239ccac22334c605871c102e114c42e58a15cb3f695743a5599"],"state_sha256":"f2f13e042e33d08de1758f81a398e90094fa5e705881f214285fa203688b965e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nNVThHWfr9e9Z+3P/XtPkjOhN4vTvFqDvVT4fR573Dxk6zQ03rRwSm/5zc53dYEa7Mj0chEMYWPIovYQpS3wDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T23:01:30.428117Z","bundle_sha256":"4e3c1b2c31a9758e458389fdbec085f8e4e1de977c0ff716d14333b144bb0b07"}}