{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:DMHVRHKLGIA3CCJ4DUHZBHOQJW","short_pith_number":"pith:DMHVRHKL","canonical_record":{"source":{"id":"1511.02126","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-06T15:51:07Z","cross_cats_sorted":[],"title_canon_sha256":"9cc2d35af76418181b38ec2de1753df83e49f9c0bba3acee8989d138d98dac54","abstract_canon_sha256":"392e7c468c3e38e54723ec54aab51faeb33eb1e8af78826bdd341055d31c7c27"},"schema_version":"1.0"},"canonical_sha256":"1b0f589d4b3201b1093c1d0f909dd04db456cc0c1cc2715a5f242262897f23db","source":{"kind":"arxiv","id":"1511.02126","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02126","created_at":"2026-05-18T01:27:37Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02126v1","created_at":"2026-05-18T01:27:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02126","created_at":"2026-05-18T01:27:37Z"},{"alias_kind":"pith_short_12","alias_value":"DMHVRHKLGIA3","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"DMHVRHKLGIA3CCJ4","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"DMHVRHKL","created_at":"2026-05-18T12:29:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:DMHVRHKLGIA3CCJ4DUHZBHOQJW","target":"record","payload":{"canonical_record":{"source":{"id":"1511.02126","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-06T15:51:07Z","cross_cats_sorted":[],"title_canon_sha256":"9cc2d35af76418181b38ec2de1753df83e49f9c0bba3acee8989d138d98dac54","abstract_canon_sha256":"392e7c468c3e38e54723ec54aab51faeb33eb1e8af78826bdd341055d31c7c27"},"schema_version":"1.0"},"canonical_sha256":"1b0f589d4b3201b1093c1d0f909dd04db456cc0c1cc2715a5f242262897f23db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:37.716973Z","signature_b64":"zQADv1rDQNNsdZp+Z6/3Ii0fxFtq54Tmgo51TxuCfcpoEqmb4TI6lGmm2kaJrvsZOgFvHVlgDuxu4kLCSSV4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b0f589d4b3201b1093c1d0f909dd04db456cc0c1cc2715a5f242262897f23db","last_reissued_at":"2026-05-18T01:27:37.716398Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:37.716398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.02126","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":"2026-05-18T01:27:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9KxnNFPXaYTtWRQ0rtbv10UehYky+ZRv6aTCvnSQKU4KYyVuaXTSUulduITkSck9YtPoj4Tf7vizeCXoiZ/NBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T00:05:00.134791Z"},"content_sha256":"45311855d19ea3b121453e611a9ae6c8ac440d328672382fe913e7724665693e","schema_version":"1.0","event_id":"sha256:45311855d19ea3b121453e611a9ae6c8ac440d328672382fe913e7724665693e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:DMHVRHKLGIA3CCJ4DUHZBHOQJW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pooling the Convolutional Layers in Deep ConvNets for Action Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Richang Hong, Shichao Zhao, Yahong Han, Yanbin Liu","submitted_at":"2015-11-06T15:51:07Z","abstract_excerpt":"Deep ConvNets have shown its good performance in image classification tasks. However it still remains as a problem in deep video representation for action recognition. The problem comes from two aspects: on one hand, current video ConvNets are relatively shallow compared with image ConvNets, which limits its capability of capturing the complex video action information; on the other hand, temporal information of videos is not properly utilized to pool and encode the video sequences. Towards these issues, in this paper, we utilize two state-of-the-art ConvNets, i.e., the very deep spatial net (V"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02126","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":"2026-05-18T01:27:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aIYPvP1YsAE9iofdb4CgkoT/gi74pDWp+4ISqwSmnNIOQcMPZzGFD+A4CK14UemFQ7pCe4BjGFFGr9bIxLDpCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T00:05:00.135124Z"},"content_sha256":"8322dd509e51192460148a6ea7f526c99d83ad15facce4f0cbf88fe34c60c9b0","schema_version":"1.0","event_id":"sha256:8322dd509e51192460148a6ea7f526c99d83ad15facce4f0cbf88fe34c60c9b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW/bundle.json","state_url":"https://pith.science/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW/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":"2026-07-05T00:05:00Z","links":{"resolver":"https://pith.science/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW","bundle":"https://pith.science/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW/bundle.json","state":"https://pith.science/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DMHVRHKLGIA3CCJ4DUHZBHOQJW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:DMHVRHKLGIA3CCJ4DUHZBHOQJW","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":"392e7c468c3e38e54723ec54aab51faeb33eb1e8af78826bdd341055d31c7c27","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-06T15:51:07Z","title_canon_sha256":"9cc2d35af76418181b38ec2de1753df83e49f9c0bba3acee8989d138d98dac54"},"schema_version":"1.0","source":{"id":"1511.02126","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02126","created_at":"2026-05-18T01:27:37Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02126v1","created_at":"2026-05-18T01:27:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02126","created_at":"2026-05-18T01:27:37Z"},{"alias_kind":"pith_short_12","alias_value":"DMHVRHKLGIA3","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"DMHVRHKLGIA3CCJ4","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"DMHVRHKL","created_at":"2026-05-18T12:29:17Z"}],"graph_snapshots":[{"event_id":"sha256:8322dd509e51192460148a6ea7f526c99d83ad15facce4f0cbf88fe34c60c9b0","target":"graph","created_at":"2026-05-18T01:27:37Z","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":"Deep ConvNets have shown its good performance in image classification tasks. However it still remains as a problem in deep video representation for action recognition. The problem comes from two aspects: on one hand, current video ConvNets are relatively shallow compared with image ConvNets, which limits its capability of capturing the complex video action information; on the other hand, temporal information of videos is not properly utilized to pool and encode the video sequences. Towards these issues, in this paper, we utilize two state-of-the-art ConvNets, i.e., the very deep spatial net (V","authors_text":"Richang Hong, Shichao Zhao, Yahong Han, Yanbin Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-06T15:51:07Z","title":"Pooling the Convolutional Layers in Deep ConvNets for Action Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02126","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":"sha256:45311855d19ea3b121453e611a9ae6c8ac440d328672382fe913e7724665693e","target":"record","created_at":"2026-05-18T01:27:37Z","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":"392e7c468c3e38e54723ec54aab51faeb33eb1e8af78826bdd341055d31c7c27","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-06T15:51:07Z","title_canon_sha256":"9cc2d35af76418181b38ec2de1753df83e49f9c0bba3acee8989d138d98dac54"},"schema_version":"1.0","source":{"id":"1511.02126","kind":"arxiv","version":1}},"canonical_sha256":"1b0f589d4b3201b1093c1d0f909dd04db456cc0c1cc2715a5f242262897f23db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b0f589d4b3201b1093c1d0f909dd04db456cc0c1cc2715a5f242262897f23db","first_computed_at":"2026-05-18T01:27:37.716398Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:27:37.716398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zQADv1rDQNNsdZp+Z6/3Ii0fxFtq54Tmgo51TxuCfcpoEqmb4TI6lGmm2kaJrvsZOgFvHVlgDuxu4kLCSSV4BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:27:37.716973Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.02126","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45311855d19ea3b121453e611a9ae6c8ac440d328672382fe913e7724665693e","sha256:8322dd509e51192460148a6ea7f526c99d83ad15facce4f0cbf88fe34c60c9b0"],"state_sha256":"c2cd041022acc093689387c9f8f8f923209b8e96f93111b4cf7aa2cc84ba9066"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R99rFdqmjUT4TEAgIICXZ10mxptxVwxisAD5D1jqnZBC0DOOUxlyIQASw7r1TpbHk1xH6rYQKYsfOfYmCdeRDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T00:05:00.137038Z","bundle_sha256":"df639c4bca39979208428a1a6857e25a5ce358a3636fa73f408ccdf673a20300"}}