{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:GJUGYXEXDAYA2GGBJIOT5TZVMU","short_pith_number":"pith:GJUGYXEX","schema_version":"1.0","canonical_sha256":"32686c5c9718300d18c14a1d3ecf35650a431a2be91fd0d4fe5a94c07f231a6d","source":{"kind":"arxiv","id":"1511.05292","version":3},"attestation_state":"computed","paper":{"title":"Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gang Wang, Jinghua Wang","submitted_at":"2015-11-17T07:21:20Z","abstract_excerpt":"Recognizing actions from still images is popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside sub-images and models the corre"},"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":"1511.05292","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-17T07:21:20Z","cross_cats_sorted":[],"title_canon_sha256":"9c186f7261ed6ae84edee8123aa70a6123c757e706afff35f5aa0e441a6dbdac","abstract_canon_sha256":"3778b281986632f6ae86dbac1d9d5813a71f7af2947bf2ec07aaaaee0c95d731"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:21.643805Z","signature_b64":"0p7HRAqmGJ+X/lDCVCo2riB9JAVa7tPdkNVKkxnKx3Pe3+ZRrWLVeb8Qu020/oniazFb2BsjH/60G7qxOTq+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32686c5c9718300d18c14a1d3ecf35650a431a2be91fd0d4fe5a94c07f231a6d","last_reissued_at":"2026-05-18T01:11:21.643355Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:21.643355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gang Wang, Jinghua Wang","submitted_at":"2015-11-17T07:21:20Z","abstract_excerpt":"Recognizing actions from still images is popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside sub-images and models the corre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05292","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1511.05292","created_at":"2026-05-18T01:11:21.643417+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.05292v3","created_at":"2026-05-18T01:11:21.643417+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05292","created_at":"2026-05-18T01:11:21.643417+00:00"},{"alias_kind":"pith_short_12","alias_value":"GJUGYXEXDAYA","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"GJUGYXEXDAYA2GGB","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"GJUGYXEX","created_at":"2026-05-18T12:29:22.688609+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU","json":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU.json","graph_json":"https://pith.science/api/pith-number/GJUGYXEXDAYA2GGBJIOT5TZVMU/graph.json","events_json":"https://pith.science/api/pith-number/GJUGYXEXDAYA2GGBJIOT5TZVMU/events.json","paper":"https://pith.science/paper/GJUGYXEX"},"agent_actions":{"view_html":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU","download_json":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU.json","view_paper":"https://pith.science/paper/GJUGYXEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.05292&json=true","fetch_graph":"https://pith.science/api/pith-number/GJUGYXEXDAYA2GGBJIOT5TZVMU/graph.json","fetch_events":"https://pith.science/api/pith-number/GJUGYXEXDAYA2GGBJIOT5TZVMU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU/action/storage_attestation","attest_author":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU/action/author_attestation","sign_citation":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU/action/citation_signature","submit_replication":"https://pith.science/pith/GJUGYXEXDAYA2GGBJIOT5TZVMU/action/replication_record"}},"created_at":"2026-05-18T01:11:21.643417+00:00","updated_at":"2026-05-18T01:11:21.643417+00:00"}