{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:E6GKUTD66JTCGXUDRWLRF43PPF","short_pith_number":"pith:E6GKUTD6","canonical_record":{"source":{"id":"1811.10984","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T13:57:17Z","cross_cats_sorted":[],"title_canon_sha256":"0ed0132c1ba9dc6dcfa0feced87f3806c82b8d17456c08799dac015f07e7053a","abstract_canon_sha256":"d6718e81e522eee9feabf8969d2912d3d79112eee18b74ada715e4de7513f1ef"},"schema_version":"1.0"},"canonical_sha256":"278caa4c7ef266235e838d9712f36f795423dfe92e0fec2b073b54f8fd25b5e6","source":{"kind":"arxiv","id":"1811.10984","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10984","created_at":"2026-05-17T23:59:45Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10984v1","created_at":"2026-05-17T23:59:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10984","created_at":"2026-05-17T23:59:45Z"},{"alias_kind":"pith_short_12","alias_value":"E6GKUTD66JTC","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"E6GKUTD66JTCGXUD","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"E6GKUTD6","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:E6GKUTD66JTCGXUDRWLRF43PPF","target":"record","payload":{"canonical_record":{"source":{"id":"1811.10984","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T13:57:17Z","cross_cats_sorted":[],"title_canon_sha256":"0ed0132c1ba9dc6dcfa0feced87f3806c82b8d17456c08799dac015f07e7053a","abstract_canon_sha256":"d6718e81e522eee9feabf8969d2912d3d79112eee18b74ada715e4de7513f1ef"},"schema_version":"1.0"},"canonical_sha256":"278caa4c7ef266235e838d9712f36f795423dfe92e0fec2b073b54f8fd25b5e6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:45.562220Z","signature_b64":"0FbbSsw0eAf4+Hgb07VWdnwiAdV0ndvVgsH+Ey1GTasba0b7Emj31O8s6e6CmcSvYAAPDTRJa7mgxFOlDTSjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"278caa4c7ef266235e838d9712f36f795423dfe92e0fec2b073b54f8fd25b5e6","last_reissued_at":"2026-05-17T23:59:45.561855Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:45.561855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.10984","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":"2026-05-17T23:59:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pdw7WoTLIcaVPSmAOaKu9I1BlvQJwlVtKrxHIgRdutQEZn1B4KgtqL6dSQFizal98wOPrv3merDSDtsGiWfhCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T16:19:55.874073Z"},"content_sha256":"2ef18941db5d364003d5c80a3f0bf7f832d5431549261efb8d594f3240f464a4","schema_version":"1.0","event_id":"sha256:2ef18941db5d364003d5c80a3f0bf7f832d5431549261efb8d594f3240f464a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:E6GKUTD66JTCGXUDRWLRF43PPF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrii Maksai, Pascal Fua","submitted_at":"2018-11-27T13:57:17Z","abstract_excerpt":"Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10984","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":"2026-05-17T23:59:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4Ku1nH8a/3yYPr0KgBZ+MLRc1qSeNqpR2Mu7kQ9Jf+UZJLz67pPlFMj9E7gMkQJnt4DM6xZWYZ5nxdYHQmi2DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T16:19:55.874430Z"},"content_sha256":"9d1b6e4de28d748a4bc06e28e6752e71d5c2a16b4b830cd28c17ee2e3b199bda","schema_version":"1.0","event_id":"sha256:9d1b6e4de28d748a4bc06e28e6752e71d5c2a16b4b830cd28c17ee2e3b199bda"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E6GKUTD66JTCGXUDRWLRF43PPF/bundle.json","state_url":"https://pith.science/pith/E6GKUTD66JTCGXUDRWLRF43PPF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E6GKUTD66JTCGXUDRWLRF43PPF/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":"2026-06-02T16:19:55Z","links":{"resolver":"https://pith.science/pith/E6GKUTD66JTCGXUDRWLRF43PPF","bundle":"https://pith.science/pith/E6GKUTD66JTCGXUDRWLRF43PPF/bundle.json","state":"https://pith.science/pith/E6GKUTD66JTCGXUDRWLRF43PPF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E6GKUTD66JTCGXUDRWLRF43PPF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:E6GKUTD66JTCGXUDRWLRF43PPF","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":"d6718e81e522eee9feabf8969d2912d3d79112eee18b74ada715e4de7513f1ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T13:57:17Z","title_canon_sha256":"0ed0132c1ba9dc6dcfa0feced87f3806c82b8d17456c08799dac015f07e7053a"},"schema_version":"1.0","source":{"id":"1811.10984","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10984","created_at":"2026-05-17T23:59:45Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10984v1","created_at":"2026-05-17T23:59:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10984","created_at":"2026-05-17T23:59:45Z"},{"alias_kind":"pith_short_12","alias_value":"E6GKUTD66JTC","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"E6GKUTD66JTCGXUD","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"E6GKUTD6","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:9d1b6e4de28d748a4bc06e28e6752e71d5c2a16b4b830cd28c17ee2e3b199bda","target":"graph","created_at":"2026-05-17T23:59:45Z","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":"Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit pro","authors_text":"Andrii Maksai, Pascal Fua","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T13:57:17Z","title":"Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10984","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":"sha256:2ef18941db5d364003d5c80a3f0bf7f832d5431549261efb8d594f3240f464a4","target":"record","created_at":"2026-05-17T23:59:45Z","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":"d6718e81e522eee9feabf8969d2912d3d79112eee18b74ada715e4de7513f1ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T13:57:17Z","title_canon_sha256":"0ed0132c1ba9dc6dcfa0feced87f3806c82b8d17456c08799dac015f07e7053a"},"schema_version":"1.0","source":{"id":"1811.10984","kind":"arxiv","version":1}},"canonical_sha256":"278caa4c7ef266235e838d9712f36f795423dfe92e0fec2b073b54f8fd25b5e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"278caa4c7ef266235e838d9712f36f795423dfe92e0fec2b073b54f8fd25b5e6","first_computed_at":"2026-05-17T23:59:45.561855Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:45.561855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0FbbSsw0eAf4+Hgb07VWdnwiAdV0ndvVgsH+Ey1GTasba0b7Emj31O8s6e6CmcSvYAAPDTRJa7mgxFOlDTSjAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:45.562220Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.10984","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ef18941db5d364003d5c80a3f0bf7f832d5431549261efb8d594f3240f464a4","sha256:9d1b6e4de28d748a4bc06e28e6752e71d5c2a16b4b830cd28c17ee2e3b199bda"],"state_sha256":"2081e3449c7db8c4714b4b31f1ae01e8e1a925d5ca1d18066d0e6cdc57eb8c01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w8JHHX6pWungrjnZ93eorUcCNvWRcQMeDQ2IjqxRM+Jlncxd+yhfwOqKT/PkXaBHjEpCRbxUn48HItZR5e17DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T16:19:55.876361Z","bundle_sha256":"9b3dbfab27337527dbd1e371daea50cbc145fb63b5df4b55508b5720bbf3eff8"}}