{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:DJL5DPDSIQLBI7VNN4BLZ5HDND","short_pith_number":"pith:DJL5DPDS","canonical_record":{"source":{"id":"1604.08660","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-29T00:55:41Z","cross_cats_sorted":[],"title_canon_sha256":"c724e1fc8d11b2890728a7a0446bff7118497bc187a2806630c2d8a5c0830dff","abstract_canon_sha256":"83109e64148298c6364f03e845febe07a8fa1e2873907d323be548f47135e8b0"},"schema_version":"1.0"},"canonical_sha256":"1a57d1bc724416147ead6f02bcf4e368e33d03e89cac8a6ebccce12ef62b2112","source":{"kind":"arxiv","id":"1604.08660","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.08660","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"arxiv_version","alias_value":"1604.08660v1","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.08660","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"pith_short_12","alias_value":"DJL5DPDSIQLB","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"DJL5DPDSIQLBI7VN","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"DJL5DPDS","created_at":"2026-05-18T12:30:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:DJL5DPDSIQLBI7VNN4BLZ5HDND","target":"record","payload":{"canonical_record":{"source":{"id":"1604.08660","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-29T00:55:41Z","cross_cats_sorted":[],"title_canon_sha256":"c724e1fc8d11b2890728a7a0446bff7118497bc187a2806630c2d8a5c0830dff","abstract_canon_sha256":"83109e64148298c6364f03e845febe07a8fa1e2873907d323be548f47135e8b0"},"schema_version":"1.0"},"canonical_sha256":"1a57d1bc724416147ead6f02bcf4e368e33d03e89cac8a6ebccce12ef62b2112","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:16:03.195359Z","signature_b64":"m7pA1Yar19Yr2okFxFPKyQs88aAxjExqnDqOLoPBHJTbkdCNy/nRIkWrnAsHRlV2yaC6DkhhHeqdoOzVPtIHDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a57d1bc724416147ead6f02bcf4e368e33d03e89cac8a6ebccce12ef62b2112","last_reissued_at":"2026-05-18T01:16:03.194724Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:16:03.194724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.08660","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":"2026-05-18T01:16:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ypEUxm9R5CnoDAof+evbBvAUclvxlEiANUsfz+EKHgqsxPDSqHV/QHythzQVERz9E6eUJIWUUKeKLauq2oNpCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:55:17.604307Z"},"content_sha256":"3e6a5e502b30fd6faffa5b92bcd3ba5dcc9f158fa81a287a8993eeb7571cb110","schema_version":"1.0","event_id":"sha256:3e6a5e502b30fd6faffa5b92bcd3ba5dcc9f158fa81a287a8993eeb7571cb110"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:DJL5DPDSIQLBI7VNN4BLZ5HDND","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Biyun Sheng, Changyin Sun, Chunhua Shen, Guosheng Lin, Jun Li, Wankou Yang","submitted_at":"2016-04-29T00:55:41Z","abstract_excerpt":"Crowd counting is an important task in computer vision, which has many applications in video surveillance. Although the regression-based framework has achieved great improvements for crowd counting, how to improve the discriminative power of image representation is still an open problem. Conventional holistic features used in crowd counting often fail to capture semantic attributes and spatial cues of the image. In this paper, we propose integrating semantic information into learning locality-aware feature sets for accurate crowd counting. First, with the help of convolutional neural network ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.08660","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":"2026-05-18T01:16:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DPvgYDbkQKxDx0bBU5MGETp3e8GBPoLw/PnewsXoC5X7qKv1Yh9HXCKk6cKtiyyi+Pmm47kwcyxq0O2Ape1EDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:55:17.604945Z"},"content_sha256":"cc6d36b2db6ffa28e17631d4b5b1c76414f93595923cf75da0876e65122caa16","schema_version":"1.0","event_id":"sha256:cc6d36b2db6ffa28e17631d4b5b1c76414f93595923cf75da0876e65122caa16"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND/bundle.json","state_url":"https://pith.science/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND/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":"2026-05-26T11:55:17Z","links":{"resolver":"https://pith.science/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND","bundle":"https://pith.science/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND/bundle.json","state":"https://pith.science/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DJL5DPDSIQLBI7VNN4BLZ5HDND/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:DJL5DPDSIQLBI7VNN4BLZ5HDND","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":"83109e64148298c6364f03e845febe07a8fa1e2873907d323be548f47135e8b0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-29T00:55:41Z","title_canon_sha256":"c724e1fc8d11b2890728a7a0446bff7118497bc187a2806630c2d8a5c0830dff"},"schema_version":"1.0","source":{"id":"1604.08660","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.08660","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"arxiv_version","alias_value":"1604.08660v1","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.08660","created_at":"2026-05-18T01:16:03Z"},{"alias_kind":"pith_short_12","alias_value":"DJL5DPDSIQLB","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"DJL5DPDSIQLBI7VN","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"DJL5DPDS","created_at":"2026-05-18T12:30:12Z"}],"graph_snapshots":[{"event_id":"sha256:cc6d36b2db6ffa28e17631d4b5b1c76414f93595923cf75da0876e65122caa16","target":"graph","created_at":"2026-05-18T01:16:03Z","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":"Crowd counting is an important task in computer vision, which has many applications in video surveillance. Although the regression-based framework has achieved great improvements for crowd counting, how to improve the discriminative power of image representation is still an open problem. Conventional holistic features used in crowd counting often fail to capture semantic attributes and spatial cues of the image. In this paper, we propose integrating semantic information into learning locality-aware feature sets for accurate crowd counting. First, with the help of convolutional neural network (","authors_text":"Biyun Sheng, Changyin Sun, Chunhua Shen, Guosheng Lin, Jun Li, Wankou Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-29T00:55:41Z","title":"Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.08660","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":"sha256:3e6a5e502b30fd6faffa5b92bcd3ba5dcc9f158fa81a287a8993eeb7571cb110","target":"record","created_at":"2026-05-18T01:16:03Z","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":"83109e64148298c6364f03e845febe07a8fa1e2873907d323be548f47135e8b0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-29T00:55:41Z","title_canon_sha256":"c724e1fc8d11b2890728a7a0446bff7118497bc187a2806630c2d8a5c0830dff"},"schema_version":"1.0","source":{"id":"1604.08660","kind":"arxiv","version":1}},"canonical_sha256":"1a57d1bc724416147ead6f02bcf4e368e33d03e89cac8a6ebccce12ef62b2112","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a57d1bc724416147ead6f02bcf4e368e33d03e89cac8a6ebccce12ef62b2112","first_computed_at":"2026-05-18T01:16:03.194724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:16:03.194724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"m7pA1Yar19Yr2okFxFPKyQs88aAxjExqnDqOLoPBHJTbkdCNy/nRIkWrnAsHRlV2yaC6DkhhHeqdoOzVPtIHDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:16:03.195359Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.08660","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e6a5e502b30fd6faffa5b92bcd3ba5dcc9f158fa81a287a8993eeb7571cb110","sha256:cc6d36b2db6ffa28e17631d4b5b1c76414f93595923cf75da0876e65122caa16"],"state_sha256":"f8a9059e51e37da8f18a36b7d1eed16cd7180963e8744d21ce903cc4f35922e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j2FI2cncj/kfwejJ32LcsXdNAqLOKz8wVk57cMnlNpKTDa2oLf/Y1dscfd5B+4vKtMBro/0ApQtS+9Uh1Ad4BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:55:17.609053Z","bundle_sha256":"8e1667a2f4cec4ff81bff324a71b908c54cb8abf690b0f014dd9a6e75fabd95b"}}