{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7GSL47HSEDTPXUVAJI4XAMAUHG","short_pith_number":"pith:7GSL47HS","canonical_record":{"source":{"id":"1705.08764","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-24T13:52:23Z","cross_cats_sorted":[],"title_canon_sha256":"935078c3e4a61e64e71478813e275c0f652a1bf7a5ff02fc9825f88ab447d964","abstract_canon_sha256":"e7319d91e4da28b16ba5831143c22d094238b63b98749a1399c0c4c9e1288abf"},"schema_version":"1.0"},"canonical_sha256":"f9a4be7cf220e6fbd2a04a3970301439a307565bbaa412dd8786bd12570d7745","source":{"kind":"arxiv","id":"1705.08764","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08764","created_at":"2026-05-18T00:43:44Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08764v1","created_at":"2026-05-18T00:43:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08764","created_at":"2026-05-18T00:43:44Z"},{"alias_kind":"pith_short_12","alias_value":"7GSL47HSEDTP","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7GSL47HSEDTPXUVA","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7GSL47HS","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7GSL47HSEDTPXUVAJI4XAMAUHG","target":"record","payload":{"canonical_record":{"source":{"id":"1705.08764","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-24T13:52:23Z","cross_cats_sorted":[],"title_canon_sha256":"935078c3e4a61e64e71478813e275c0f652a1bf7a5ff02fc9825f88ab447d964","abstract_canon_sha256":"e7319d91e4da28b16ba5831143c22d094238b63b98749a1399c0c4c9e1288abf"},"schema_version":"1.0"},"canonical_sha256":"f9a4be7cf220e6fbd2a04a3970301439a307565bbaa412dd8786bd12570d7745","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:44.334390Z","signature_b64":"E/RJ3wzqAvyne0raqfluVXweWSatKAnIBCZCmCGkjduJ5O3PMBFv60Ugu03uaZVl9EwkY6Q+AzD/PiMchJqkAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9a4be7cf220e6fbd2a04a3970301439a307565bbaa412dd8786bd12570d7745","last_reissued_at":"2026-05-18T00:43:44.333860Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:44.333860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.08764","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":"2026-05-18T00:43:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lkUJU5EoA78qfHf2FVFUijE0R9lZW5RbJ/Cenb/5ZxB3VZXtrzR3knjbky1SMpvpa6GRnHsOQ3mkzmUcbk2lDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T17:26:30.539495Z"},"content_sha256":"9f18a30c87f4fe9b1708bcbdd98004c9f48bcc2d1b14da69f35ebe7233c246e5","schema_version":"1.0","event_id":"sha256:9f18a30c87f4fe9b1708bcbdd98004c9f48bcc2d1b14da69f35ebe7233c246e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7GSL47HSEDTPXUVAJI4XAMAUHG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haanvid Lee, Jun Tani, Minju Jung","submitted_at":"2017-05-24T13:52:23Z","abstract_excerpt":"Based on the progress of image recognition, video recognition has been extensively studied recently. However, most of the existing methods are focused on short-term but not long-term video recognition, called contextual video recognition. To address contextual video recognition, we use convolutional recurrent neural networks (ConvRNNs) having a rich spatio-temporal information processing capability, but ConvRNNs requires extensive computation that slows down training. In this paper, inspired by the normalization and detrending methods, we propose adaptive detrending (AD) for temporal normaliza"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08764","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":"2026-05-18T00:43:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GLfb/VH0+1glrqFGSt/YKppUNLeA165iRPiMUxHauzQYzVts0FI9akJlrBiB4mRUImejfs22dGVYWZZLycM9Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T17:26:30.539887Z"},"content_sha256":"c4cb79023517bc65b197fc3b7e4d4cdf04eea7cc9ba9be672cba8aecfa9017c7","schema_version":"1.0","event_id":"sha256:c4cb79023517bc65b197fc3b7e4d4cdf04eea7cc9ba9be672cba8aecfa9017c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7GSL47HSEDTPXUVAJI4XAMAUHG/bundle.json","state_url":"https://pith.science/pith/7GSL47HSEDTPXUVAJI4XAMAUHG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7GSL47HSEDTPXUVAJI4XAMAUHG/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":"2026-05-29T17:26:30Z","links":{"resolver":"https://pith.science/pith/7GSL47HSEDTPXUVAJI4XAMAUHG","bundle":"https://pith.science/pith/7GSL47HSEDTPXUVAJI4XAMAUHG/bundle.json","state":"https://pith.science/pith/7GSL47HSEDTPXUVAJI4XAMAUHG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7GSL47HSEDTPXUVAJI4XAMAUHG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7GSL47HSEDTPXUVAJI4XAMAUHG","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":"e7319d91e4da28b16ba5831143c22d094238b63b98749a1399c0c4c9e1288abf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-24T13:52:23Z","title_canon_sha256":"935078c3e4a61e64e71478813e275c0f652a1bf7a5ff02fc9825f88ab447d964"},"schema_version":"1.0","source":{"id":"1705.08764","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08764","created_at":"2026-05-18T00:43:44Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08764v1","created_at":"2026-05-18T00:43:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08764","created_at":"2026-05-18T00:43:44Z"},{"alias_kind":"pith_short_12","alias_value":"7GSL47HSEDTP","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7GSL47HSEDTPXUVA","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7GSL47HS","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:c4cb79023517bc65b197fc3b7e4d4cdf04eea7cc9ba9be672cba8aecfa9017c7","target":"graph","created_at":"2026-05-18T00:43:44Z","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":"Based on the progress of image recognition, video recognition has been extensively studied recently. However, most of the existing methods are focused on short-term but not long-term video recognition, called contextual video recognition. To address contextual video recognition, we use convolutional recurrent neural networks (ConvRNNs) having a rich spatio-temporal information processing capability, but ConvRNNs requires extensive computation that slows down training. In this paper, inspired by the normalization and detrending methods, we propose adaptive detrending (AD) for temporal normaliza","authors_text":"Haanvid Lee, Jun Tani, Minju Jung","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-24T13:52:23Z","title":"Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08764","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":"sha256:9f18a30c87f4fe9b1708bcbdd98004c9f48bcc2d1b14da69f35ebe7233c246e5","target":"record","created_at":"2026-05-18T00:43:44Z","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":"e7319d91e4da28b16ba5831143c22d094238b63b98749a1399c0c4c9e1288abf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-24T13:52:23Z","title_canon_sha256":"935078c3e4a61e64e71478813e275c0f652a1bf7a5ff02fc9825f88ab447d964"},"schema_version":"1.0","source":{"id":"1705.08764","kind":"arxiv","version":1}},"canonical_sha256":"f9a4be7cf220e6fbd2a04a3970301439a307565bbaa412dd8786bd12570d7745","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9a4be7cf220e6fbd2a04a3970301439a307565bbaa412dd8786bd12570d7745","first_computed_at":"2026-05-18T00:43:44.333860Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:44.333860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E/RJ3wzqAvyne0raqfluVXweWSatKAnIBCZCmCGkjduJ5O3PMBFv60Ugu03uaZVl9EwkY6Q+AzD/PiMchJqkAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:44.334390Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.08764","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9f18a30c87f4fe9b1708bcbdd98004c9f48bcc2d1b14da69f35ebe7233c246e5","sha256:c4cb79023517bc65b197fc3b7e4d4cdf04eea7cc9ba9be672cba8aecfa9017c7"],"state_sha256":"8babbaaa102851d76fb59e7b7c397632bb0136b83dd61925d1387b512d8dc1b0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KMVjBJmGMKI2rG3eo9cv2tauF/Wgj/M6s+s/OV/wW4QjrXE3d/22Ei5zP15Yo4xVxZ4UvEybkzHCsb40gv/DCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T17:26:30.543184Z","bundle_sha256":"cadd96a08a0f1a5a5ed7451e1f6c505e9c3df10828254cb81efd36e4fd77dc3d"}}