{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KB6AQ5U4FFEOW2HKWMODSSGMMX","short_pith_number":"pith:KB6AQ5U4","schema_version":"1.0","canonical_sha256":"507c08769c2948eb68eab31c3948cc65c28bfb795c199b4b90ce154e2aba0d8b","source":{"kind":"arxiv","id":"1907.10255","version":1},"attestation_state":"computed","paper":{"title":"HA-CCN: Hierarchical Attention-based Crowd Counting Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Vishal M. Patel, Vishwanath A. Sindagi","submitted_at":"2019-07-24T06:20:14Z","abstract_excerpt":"Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). SAM enhances low-level features in the network by infusing spatial segmentation information, whereas "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1907.10255","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-24T06:20:14Z","cross_cats_sorted":[],"title_canon_sha256":"5b1d00a68bbb3f152da11d91c92b28cffc55b43f83393aad6b0a7c7282543f26","abstract_canon_sha256":"e0de8d851b6b005cd2f7598b2327c0d68eb760d241744c8ec3290e72def63c29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:51.745619Z","signature_b64":"JrQ83TILn9wQp72rAn6ke3OWGVc4VT3bAE1/8kzwownOpdFIcUWgVVxrH2YcUziWKUGxnceJPksrFPJJIGRsDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"507c08769c2948eb68eab31c3948cc65c28bfb795c199b4b90ce154e2aba0d8b","last_reissued_at":"2026-07-05T00:13:51.745226Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:51.745226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HA-CCN: Hierarchical Attention-based Crowd Counting Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Vishal M. Patel, Vishwanath A. Sindagi","submitted_at":"2019-07-24T06:20:14Z","abstract_excerpt":"Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). SAM enhances low-level features in the network by infusing spatial segmentation information, whereas "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10255","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/1907.10255/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1907.10255","created_at":"2026-07-05T00:13:51.745281+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.10255v1","created_at":"2026-07-05T00:13:51.745281+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10255","created_at":"2026-07-05T00:13:51.745281+00:00"},{"alias_kind":"pith_short_12","alias_value":"KB6AQ5U4FFEO","created_at":"2026-07-05T00:13:51.745281+00:00"},{"alias_kind":"pith_short_16","alias_value":"KB6AQ5U4FFEOW2HK","created_at":"2026-07-05T00:13:51.745281+00:00"},{"alias_kind":"pith_short_8","alias_value":"KB6AQ5U4","created_at":"2026-07-05T00:13:51.745281+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.10937","citing_title":"Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX","json":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX.json","graph_json":"https://pith.science/api/pith-number/KB6AQ5U4FFEOW2HKWMODSSGMMX/graph.json","events_json":"https://pith.science/api/pith-number/KB6AQ5U4FFEOW2HKWMODSSGMMX/events.json","paper":"https://pith.science/paper/KB6AQ5U4"},"agent_actions":{"view_html":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX","download_json":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX.json","view_paper":"https://pith.science/paper/KB6AQ5U4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.10255&json=true","fetch_graph":"https://pith.science/api/pith-number/KB6AQ5U4FFEOW2HKWMODSSGMMX/graph.json","fetch_events":"https://pith.science/api/pith-number/KB6AQ5U4FFEOW2HKWMODSSGMMX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX/action/storage_attestation","attest_author":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX/action/author_attestation","sign_citation":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX/action/citation_signature","submit_replication":"https://pith.science/pith/KB6AQ5U4FFEOW2HKWMODSSGMMX/action/replication_record"}},"created_at":"2026-07-05T00:13:51.745281+00:00","updated_at":"2026-07-05T00:13:51.745281+00:00"}