{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:E3NXNVXB2SIY4LU2KMJ2PT5IGZ","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":"d3d9535f2be1c30e9f9d142e89bbc291be4be1140e78a32c41d48fc6befc2b5c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T14:15:28Z","title_canon_sha256":"d24472f3b9a2ed3848121ff684213c0d5a36e31da92aacbf16ce842958adb955"},"schema_version":"1.0","source":{"id":"2409.09446","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.09446","created_at":"2026-07-05T09:07:09Z"},{"alias_kind":"arxiv_version","alias_value":"2409.09446v1","created_at":"2026-07-05T09:07:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09446","created_at":"2026-07-05T09:07:09Z"},{"alias_kind":"pith_short_12","alias_value":"E3NXNVXB2SIY","created_at":"2026-07-05T09:07:09Z"},{"alias_kind":"pith_short_16","alias_value":"E3NXNVXB2SIY4LU2","created_at":"2026-07-05T09:07:09Z"},{"alias_kind":"pith_short_8","alias_value":"E3NXNVXB","created_at":"2026-07-05T09:07:09Z"}],"graph_snapshots":[{"event_id":"sha256:273e53b6040595369d8eac62786633666adba72fe0a7d20743c6d4ff76e1e00f","target":"graph","created_at":"2026-07-05T09:07:09Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.09446/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pedestrian action prediction is of great significance for many applications such as autonomous driving. However, state-of-the-art methods lack explainability to make trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations including: 1) they cannot directly apply to multi-modal cases; 2) they lack locality to attend to details in the inputs; 3) they suffer from mode collapse. These limitations are tackled accordingly through t","authors_text":"Alexander Carballo, Kazuya Takeda, Keisuke Fujii, Ming Ding, Robin Karlsson, Yan Feng","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T14:15:28Z","title":"MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09446","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:dff0d4c0ec913bcabcc64e7a39b9f2a11cbfaaa9c779d8d06192f6aebd524bf6","target":"record","created_at":"2026-07-05T09:07:09Z","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":"d3d9535f2be1c30e9f9d142e89bbc291be4be1140e78a32c41d48fc6befc2b5c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T14:15:28Z","title_canon_sha256":"d24472f3b9a2ed3848121ff684213c0d5a36e31da92aacbf16ce842958adb955"},"schema_version":"1.0","source":{"id":"2409.09446","kind":"arxiv","version":1}},"canonical_sha256":"26db76d6e1d4918e2e9a5313a7cfa83651b226c260f4c21494223c90622851b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26db76d6e1d4918e2e9a5313a7cfa83651b226c260f4c21494223c90622851b8","first_computed_at":"2026-07-05T09:07:09.914590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:07:09.914590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XiNHVVDfSqNYHrw9rUszC6jInxBrvvWRoZcfWEtiWZBaSeeAFHsPJg1bTgy7tn2mqmxPu6l8qxoCcTCkJ6HdCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:07:09.915012Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.09446","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dff0d4c0ec913bcabcc64e7a39b9f2a11cbfaaa9c779d8d06192f6aebd524bf6","sha256:273e53b6040595369d8eac62786633666adba72fe0a7d20743c6d4ff76e1e00f"],"state_sha256":"fa2617d54fdf5ff1d83a35e8336a7f1047bc428fb4b98ac558e7e204d7d2e0e7"}