{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GJPVBBZUD5TR22M6OZJBO5ZHDV","short_pith_number":"pith:GJPVBBZU","canonical_record":{"source":{"id":"1812.06576","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-17T01:48:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c0213b9ef10c2212f30ddaf73998db89dd6caa0cd271d32dd3044f57c77a1ad3","abstract_canon_sha256":"b66dd282482363a342f6311a48d56b5cb600b73f69b7395835b384df673afdea"},"schema_version":"1.0"},"canonical_sha256":"325f5087341f671d699e76521777271d707fb3eae608375eed99444fab5c5725","source":{"kind":"arxiv","id":"1812.06576","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.06576","created_at":"2026-05-17T23:58:09Z"},{"alias_kind":"arxiv_version","alias_value":"1812.06576v1","created_at":"2026-05-17T23:58:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06576","created_at":"2026-05-17T23:58:09Z"},{"alias_kind":"pith_short_12","alias_value":"GJPVBBZUD5TR","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GJPVBBZUD5TR22M6","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GJPVBBZU","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GJPVBBZUD5TR22M6OZJBO5ZHDV","target":"record","payload":{"canonical_record":{"source":{"id":"1812.06576","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-17T01:48:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c0213b9ef10c2212f30ddaf73998db89dd6caa0cd271d32dd3044f57c77a1ad3","abstract_canon_sha256":"b66dd282482363a342f6311a48d56b5cb600b73f69b7395835b384df673afdea"},"schema_version":"1.0"},"canonical_sha256":"325f5087341f671d699e76521777271d707fb3eae608375eed99444fab5c5725","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:09.841168Z","signature_b64":"9YhKAMWVCYaD/x2KbdayRSMWB63pFREWYWUEOUXiPGRXpPjYxxu95isMrJgznBUy71egeEGylM0AOZo03Jj8BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"325f5087341f671d699e76521777271d707fb3eae608375eed99444fab5c5725","last_reissued_at":"2026-05-17T23:58:09.840720Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:09.840720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.06576","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":"2026-05-17T23:58:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lgAhWIq7WpQ9uptP6eRkyc5u3TbjGyfFN9NZxfjP6W4Y0/iY1tTRdojwDVYod1fsJyNiPyVErL2FvdoHZemzDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:43:11.770797Z"},"content_sha256":"07065a54e8c8bf86d0dae9e410b6f54e78b37939b5d203b3e961b8761c423d0a","schema_version":"1.0","event_id":"sha256:07065a54e8c8bf86d0dae9e410b6f54e78b37939b5d203b3e961b8761c423d0a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GJPVBBZUD5TR22M6OZJBO5ZHDV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Incremental Triplet Margin for Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Di Xie, Liang Ma, Qiaoyong Zhong, Shiliang Pu, Yingying Zhang","submitted_at":"2018-12-17T01:48:06Z","abstract_excerpt":"Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06576","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":""},"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":"2026-05-17T23:58:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FvXJkL4rCJ2KiRgHj8aDBLVMTwEDGYdTM/MJGgtrgxJxG1comugm5WXRaVPy7KNrOQHt42Dtvwfyo6TrrTLKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:43:11.771428Z"},"content_sha256":"c1c1d255f5d7662f506284c4ad46ff6323f971babfe8aea9b44daf7e762530f7","schema_version":"1.0","event_id":"sha256:c1c1d255f5d7662f506284c4ad46ff6323f971babfe8aea9b44daf7e762530f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV/bundle.json","state_url":"https://pith.science/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV/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":"2026-05-30T16:43:11Z","links":{"resolver":"https://pith.science/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV","bundle":"https://pith.science/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV/bundle.json","state":"https://pith.science/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GJPVBBZUD5TR22M6OZJBO5ZHDV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GJPVBBZUD5TR22M6OZJBO5ZHDV","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":"b66dd282482363a342f6311a48d56b5cb600b73f69b7395835b384df673afdea","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-17T01:48:06Z","title_canon_sha256":"c0213b9ef10c2212f30ddaf73998db89dd6caa0cd271d32dd3044f57c77a1ad3"},"schema_version":"1.0","source":{"id":"1812.06576","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.06576","created_at":"2026-05-17T23:58:09Z"},{"alias_kind":"arxiv_version","alias_value":"1812.06576v1","created_at":"2026-05-17T23:58:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06576","created_at":"2026-05-17T23:58:09Z"},{"alias_kind":"pith_short_12","alias_value":"GJPVBBZUD5TR","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GJPVBBZUD5TR22M6","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GJPVBBZU","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:c1c1d255f5d7662f506284c4ad46ff6323f971babfe8aea9b44daf7e762530f7","target":"graph","created_at":"2026-05-17T23:58:09Z","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":"Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to ma","authors_text":"Di Xie, Liang Ma, Qiaoyong Zhong, Shiliang Pu, Yingying Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-17T01:48:06Z","title":"Learning Incremental Triplet Margin for Person Re-identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06576","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":"sha256:07065a54e8c8bf86d0dae9e410b6f54e78b37939b5d203b3e961b8761c423d0a","target":"record","created_at":"2026-05-17T23:58:09Z","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":"b66dd282482363a342f6311a48d56b5cb600b73f69b7395835b384df673afdea","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-17T01:48:06Z","title_canon_sha256":"c0213b9ef10c2212f30ddaf73998db89dd6caa0cd271d32dd3044f57c77a1ad3"},"schema_version":"1.0","source":{"id":"1812.06576","kind":"arxiv","version":1}},"canonical_sha256":"325f5087341f671d699e76521777271d707fb3eae608375eed99444fab5c5725","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"325f5087341f671d699e76521777271d707fb3eae608375eed99444fab5c5725","first_computed_at":"2026-05-17T23:58:09.840720Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:09.840720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9YhKAMWVCYaD/x2KbdayRSMWB63pFREWYWUEOUXiPGRXpPjYxxu95isMrJgznBUy71egeEGylM0AOZo03Jj8BQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:09.841168Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.06576","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07065a54e8c8bf86d0dae9e410b6f54e78b37939b5d203b3e961b8761c423d0a","sha256:c1c1d255f5d7662f506284c4ad46ff6323f971babfe8aea9b44daf7e762530f7"],"state_sha256":"59519a29b8e44c8c75a1fdadc947d242f7b921ee80c7f2ab5966a4845e14f2f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"15iHJIT/Z6hrb4M+U9J0cWpfpTSka/ofbQPpL1fhlpeKTM81cBkAD3ARtcqWgsELRQ6/tVB0ITZujBctxkb+Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T16:43:11.775090Z","bundle_sha256":"7c5f50334ec7db74019abd71ef673bc57898c01c0b6d24754c6bb6f4eda5c65f"}}