{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:XXEVIKB5PNTKDIRBZ7ABOI5CLL","short_pith_number":"pith:XXEVIKB5","canonical_record":{"source":{"id":"2108.11116","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-25T08:28:34Z","cross_cats_sorted":[],"title_canon_sha256":"d4b8c3d0e4f7f618de4230580723b9b33d504a3cf5675d932457da5adca65260","abstract_canon_sha256":"91cc9c5bc1690b3c4a5b7f97cd9afa52d38e825343511d8e2eba4107478a3ed4"},"schema_version":"1.0"},"canonical_sha256":"bdc954283d7b66a1a221cfc01723a25aca5cd129ddde16cc0dc72407cffdad70","source":{"kind":"arxiv","id":"2108.11116","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11116","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11116v1","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11116","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"pith_short_12","alias_value":"XXEVIKB5PNTK","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"pith_short_16","alias_value":"XXEVIKB5PNTKDIRB","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"pith_short_8","alias_value":"XXEVIKB5","created_at":"2026-07-05T03:08:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:XXEVIKB5PNTKDIRBZ7ABOI5CLL","target":"record","payload":{"canonical_record":{"source":{"id":"2108.11116","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-25T08:28:34Z","cross_cats_sorted":[],"title_canon_sha256":"d4b8c3d0e4f7f618de4230580723b9b33d504a3cf5675d932457da5adca65260","abstract_canon_sha256":"91cc9c5bc1690b3c4a5b7f97cd9afa52d38e825343511d8e2eba4107478a3ed4"},"schema_version":"1.0"},"canonical_sha256":"bdc954283d7b66a1a221cfc01723a25aca5cd129ddde16cc0dc72407cffdad70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:08:49.584847Z","signature_b64":"To/vWWCCEhPnmfiVJN3jz+eL4CJzI7oO12ufCVi+cpvNtPtPSV96aXoUP89RdozLcyULGM840OkuQHbJJ203DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdc954283d7b66a1a221cfc01723a25aca5cd129ddde16cc0dc72407cffdad70","last_reissued_at":"2026-07-05T03:08:49.584399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:08:49.584399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.11116","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":"2026-07-05T03:08:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dXz/fVI12Aj6g9TqHbj1fzsuqcsFno1wqeq4dl8uqGbhJdQ1pevhfB30lk1B0p1vsT9fBhPltptQKLkTHjHNCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:25:53.395780Z"},"content_sha256":"cdf0bcdf4189c364f1cecb3e2f110b69cb2e4b81a6c56bb5b8b7b9a52040224f","schema_version":"1.0","event_id":"sha256:cdf0bcdf4189c364f1cecb3e2f110b69cb2e4b81a6c56bb5b8b7b9a52040224f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:XXEVIKB5PNTKDIRBZ7ABOI5CLL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TransFER: Learning Relation-aware Facial Expression Representations with Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fanglei Xue, Guodong Guo, Qiangchang Wang","submitted_at":"2021-08-25T08:28:34Z","abstract_excerpt":"Facial expression recognition (FER) has received increasing interest in computer vision. We propose the TransFER model which can learn rich relation-aware local representations. It mainly consists of three components: Multi-Attention Dropping (MAD), ViT-FER, and Multi-head Self-Attention Dropping (MSAD). First, local patches play an important role in distinguishing various expressions, however, few existing works can locate discriminative and diverse local patches. This can cause serious problems when some patches are invisible due to pose variations or viewpoint changes. To address this issue"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11116","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/2108.11116/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":"2026-07-05T03:08:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"INC7Rkish4ikl6ysifqPTu/eNlhS0uoIntuHC8DFqn5CGJwc+T2AkCTKk+xUjq1rGxP9Ufuu7bH3++WB0i6SBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:25:53.396212Z"},"content_sha256":"b831637ddac45afbe116b817b2326531cc0c26adbe21b4acaa60af5b57191b25","schema_version":"1.0","event_id":"sha256:b831637ddac45afbe116b817b2326531cc0c26adbe21b4acaa60af5b57191b25"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL/bundle.json","state_url":"https://pith.science/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL/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":"2026-08-06T08:25:53Z","links":{"resolver":"https://pith.science/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL","bundle":"https://pith.science/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL/bundle.json","state":"https://pith.science/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXEVIKB5PNTKDIRBZ7ABOI5CLL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XXEVIKB5PNTKDIRBZ7ABOI5CLL","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":"91cc9c5bc1690b3c4a5b7f97cd9afa52d38e825343511d8e2eba4107478a3ed4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-25T08:28:34Z","title_canon_sha256":"d4b8c3d0e4f7f618de4230580723b9b33d504a3cf5675d932457da5adca65260"},"schema_version":"1.0","source":{"id":"2108.11116","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11116","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11116v1","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11116","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"pith_short_12","alias_value":"XXEVIKB5PNTK","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"pith_short_16","alias_value":"XXEVIKB5PNTKDIRB","created_at":"2026-07-05T03:08:49Z"},{"alias_kind":"pith_short_8","alias_value":"XXEVIKB5","created_at":"2026-07-05T03:08:49Z"}],"graph_snapshots":[{"event_id":"sha256:b831637ddac45afbe116b817b2326531cc0c26adbe21b4acaa60af5b57191b25","target":"graph","created_at":"2026-07-05T03:08:49Z","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/2108.11116/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Facial expression recognition (FER) has received increasing interest in computer vision. We propose the TransFER model which can learn rich relation-aware local representations. It mainly consists of three components: Multi-Attention Dropping (MAD), ViT-FER, and Multi-head Self-Attention Dropping (MSAD). First, local patches play an important role in distinguishing various expressions, however, few existing works can locate discriminative and diverse local patches. This can cause serious problems when some patches are invisible due to pose variations or viewpoint changes. To address this issue","authors_text":"Fanglei Xue, Guodong Guo, Qiangchang Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-25T08:28:34Z","title":"TransFER: Learning Relation-aware Facial Expression Representations with Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11116","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:cdf0bcdf4189c364f1cecb3e2f110b69cb2e4b81a6c56bb5b8b7b9a52040224f","target":"record","created_at":"2026-07-05T03:08:49Z","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":"91cc9c5bc1690b3c4a5b7f97cd9afa52d38e825343511d8e2eba4107478a3ed4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-25T08:28:34Z","title_canon_sha256":"d4b8c3d0e4f7f618de4230580723b9b33d504a3cf5675d932457da5adca65260"},"schema_version":"1.0","source":{"id":"2108.11116","kind":"arxiv","version":1}},"canonical_sha256":"bdc954283d7b66a1a221cfc01723a25aca5cd129ddde16cc0dc72407cffdad70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdc954283d7b66a1a221cfc01723a25aca5cd129ddde16cc0dc72407cffdad70","first_computed_at":"2026-07-05T03:08:49.584399Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:08:49.584399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"To/vWWCCEhPnmfiVJN3jz+eL4CJzI7oO12ufCVi+cpvNtPtPSV96aXoUP89RdozLcyULGM840OkuQHbJJ203DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:08:49.584847Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.11116","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cdf0bcdf4189c364f1cecb3e2f110b69cb2e4b81a6c56bb5b8b7b9a52040224f","sha256:b831637ddac45afbe116b817b2326531cc0c26adbe21b4acaa60af5b57191b25"],"state_sha256":"99c86646670e98708324658f9765640cd559cca86dc6deb6e3bb687382559ca0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r2a/TQDXzlnUVVm5HUvpH0d7bFgmC8v4TkKphHe1HprlN4EKTjEDSQOkJSLongmI+bkt7k1oTTiw8pORYhBgDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T08:25:53.400277Z","bundle_sha256":"67eaa0b5d75e05ea7fca0dbea50f361b416d8f437ecfaec7b733cc9376cda33f"}}