{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:INXLFP4XAIHVAJYAU7ESVK5ZY4","short_pith_number":"pith:INXLFP4X","canonical_record":{"source":{"id":"2211.07982","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-15T08:35:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"052e0e150f384a7b122272947763ec7cdfd34a383c9346c0fe8c0a39e3ce78c7","abstract_canon_sha256":"9fa76cff9bf02601b83a664e5b561dc4ca7bf4c85db2b2ee17b939e074f87f7f"},"schema_version":"1.0"},"canonical_sha256":"436eb2bf97020f502700a7c92aabb9c7239df4b0d47ca621ab2db950ebb4751d","source":{"kind":"arxiv","id":"2211.07982","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07982","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07982v1","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07982","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"pith_short_12","alias_value":"INXLFP4XAIHV","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"pith_short_16","alias_value":"INXLFP4XAIHVAJYA","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"pith_short_8","alias_value":"INXLFP4X","created_at":"2026-07-05T05:16:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:INXLFP4XAIHVAJYAU7ESVK5ZY4","target":"record","payload":{"canonical_record":{"source":{"id":"2211.07982","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-15T08:35:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"052e0e150f384a7b122272947763ec7cdfd34a383c9346c0fe8c0a39e3ce78c7","abstract_canon_sha256":"9fa76cff9bf02601b83a664e5b561dc4ca7bf4c85db2b2ee17b939e074f87f7f"},"schema_version":"1.0"},"canonical_sha256":"436eb2bf97020f502700a7c92aabb9c7239df4b0d47ca621ab2db950ebb4751d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:16:08.566498Z","signature_b64":"NB5XIdUcDw+o4LQXdbiROexaeLMjSI4cD46AIRvQSX/YCAUeHW8qu+g81NL3aCs7MrCSTiU4yvsuXk+lZ6UDDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"436eb2bf97020f502700a7c92aabb9c7239df4b0d47ca621ab2db950ebb4751d","last_reissued_at":"2026-07-05T05:16:08.566050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:16:08.566050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.07982","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":"2026-07-05T05:16:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5okijX+YpRg8tqVLFAO6Ju5zUR0YcrpF67gpPpjRBlfaxNDJ0i8xeaMIqYpe1rPKXGc15r9FXbHm528gjnaKBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:17:51.966459Z"},"content_sha256":"16a1186c97aa0158b7087e30005e112c412dc1c7084dfa752efd92fda354c606","schema_version":"1.0","event_id":"sha256:16a1186c97aa0158b7087e30005e112c412dc1c7084dfa752efd92fda354c606"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:INXLFP4XAIHVAJYAU7ESVK5ZY4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cristina Conati, Daesik Jang, Hui Hu, Matteo Rizzo","submitted_at":"2022-11-15T08:35:58Z","abstract_excerpt":"The opacity of deep learning models constrains their debugging and improvement. Augmenting deep models with saliency-based strategies, such as attention, has been claimed to help get a better understanding of the decision-making process of black-box models. However, some recent works challenged saliency's faithfulness in the field of Natural Language Processing (NLP), questioning attention weights' adherence to the true decision-making process of the model. We add to this discussion by evaluating the faithfulness of in-model saliency applied to a video processing task for the first time, namel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07982","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/2211.07982/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":"2026-07-05T05:16:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x2zn+P1y/LgIk65gSRVjixt2YVBBrgrUSsxshwbwW4akXaxTBijzT+oCc++JA35W/xGSay51qsGujrSCYglZCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:17:51.967010Z"},"content_sha256":"c7cb75a654a57de9f6ca6c74929699c5b2bc4a1618930a70305142eabe9e1274","schema_version":"1.0","event_id":"sha256:c7cb75a654a57de9f6ca6c74929699c5b2bc4a1618930a70305142eabe9e1274"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4/bundle.json","state_url":"https://pith.science/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4/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":"2026-08-04T07:17:51Z","links":{"resolver":"https://pith.science/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4","bundle":"https://pith.science/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4/bundle.json","state":"https://pith.science/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/INXLFP4XAIHVAJYAU7ESVK5ZY4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:INXLFP4XAIHVAJYAU7ESVK5ZY4","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":"9fa76cff9bf02601b83a664e5b561dc4ca7bf4c85db2b2ee17b939e074f87f7f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-15T08:35:58Z","title_canon_sha256":"052e0e150f384a7b122272947763ec7cdfd34a383c9346c0fe8c0a39e3ce78c7"},"schema_version":"1.0","source":{"id":"2211.07982","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07982","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07982v1","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07982","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"pith_short_12","alias_value":"INXLFP4XAIHV","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"pith_short_16","alias_value":"INXLFP4XAIHVAJYA","created_at":"2026-07-05T05:16:08Z"},{"alias_kind":"pith_short_8","alias_value":"INXLFP4X","created_at":"2026-07-05T05:16:08Z"}],"graph_snapshots":[{"event_id":"sha256:c7cb75a654a57de9f6ca6c74929699c5b2bc4a1618930a70305142eabe9e1274","target":"graph","created_at":"2026-07-05T05:16:08Z","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/2211.07982/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The opacity of deep learning models constrains their debugging and improvement. Augmenting deep models with saliency-based strategies, such as attention, has been claimed to help get a better understanding of the decision-making process of black-box models. However, some recent works challenged saliency's faithfulness in the field of Natural Language Processing (NLP), questioning attention weights' adherence to the true decision-making process of the model. We add to this discussion by evaluating the faithfulness of in-model saliency applied to a video processing task for the first time, namel","authors_text":"Cristina Conati, Daesik Jang, Hui Hu, Matteo Rizzo","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-15T08:35:58Z","title":"Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07982","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":"sha256:16a1186c97aa0158b7087e30005e112c412dc1c7084dfa752efd92fda354c606","target":"record","created_at":"2026-07-05T05:16:08Z","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":"9fa76cff9bf02601b83a664e5b561dc4ca7bf4c85db2b2ee17b939e074f87f7f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-15T08:35:58Z","title_canon_sha256":"052e0e150f384a7b122272947763ec7cdfd34a383c9346c0fe8c0a39e3ce78c7"},"schema_version":"1.0","source":{"id":"2211.07982","kind":"arxiv","version":1}},"canonical_sha256":"436eb2bf97020f502700a7c92aabb9c7239df4b0d47ca621ab2db950ebb4751d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"436eb2bf97020f502700a7c92aabb9c7239df4b0d47ca621ab2db950ebb4751d","first_computed_at":"2026-07-05T05:16:08.566050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:16:08.566050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NB5XIdUcDw+o4LQXdbiROexaeLMjSI4cD46AIRvQSX/YCAUeHW8qu+g81NL3aCs7MrCSTiU4yvsuXk+lZ6UDDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:16:08.566498Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.07982","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16a1186c97aa0158b7087e30005e112c412dc1c7084dfa752efd92fda354c606","sha256:c7cb75a654a57de9f6ca6c74929699c5b2bc4a1618930a70305142eabe9e1274"],"state_sha256":"c613a2396ba3e10dc3fa4acce6748a1fbb08e3e490fe9a5c780ebb706aafc247"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z2l/5YLhoFoAC0rXIqsn0XjYsHTujpcSSONVA32GkdB8xHUzqV5SHvjPs0tI6BcdZt7UIeoN8M5oh8F7YYY7CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T07:17:51.972017Z","bundle_sha256":"3dce570664b4654c3d6cc068d16c34c4922d0595431a2923a0ff476700bb0e38"}}