{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:IG2UZGZ37M3QJIJVIPUPJYKIB6","short_pith_number":"pith:IG2UZGZ3","schema_version":"1.0","canonical_sha256":"41b54c9b3bfb3704a13543e8f4e1480f9babe0d9dd633c35f9443b017bde69ae","source":{"kind":"arxiv","id":"1705.02751","version":1},"attestation_state":"computed","paper":{"title":"High-Level Concepts for Affective Understanding of Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Afsheen Rafaqat Ali, Jeffrey Ho, Mohsen Ali, Usman Shahid","submitted_at":"2017-05-08T05:58:05Z","abstract_excerpt":"This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a black-box, we use HLCs generated by pretrained CNNs in an explicit way to investigate the relations/associations between these HLCs and a (small) set of Ekman's emotional classes. As a proof-of-concept, we first propose a linear admixture model for modeling these relations, and the resulting computational framework allows us "},"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":"1705.02751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-08T05:58:05Z","cross_cats_sorted":[],"title_canon_sha256":"bf50e1f59c6676067a287f76fb29fb7770ca7a35292536f49763e66c0e7bb8ad","abstract_canon_sha256":"d584a588fc3a87fac20c92c43705313fc982a1fe84e1212c1272cbd3e8c53ab9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:53.745275Z","signature_b64":"ZDwMMiEq3BOZlEL6be700UvGOZqQ49TJ7hUORvcUcdBiixZVjIryXAN9OJoU2oYM8ERu+xvWPhCaxoz2I+FgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41b54c9b3bfb3704a13543e8f4e1480f9babe0d9dd633c35f9443b017bde69ae","last_reissued_at":"2026-05-18T00:44:53.744729Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:53.744729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-Level Concepts for Affective Understanding of Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Afsheen Rafaqat Ali, Jeffrey Ho, Mohsen Ali, Usman Shahid","submitted_at":"2017-05-08T05:58:05Z","abstract_excerpt":"This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a black-box, we use HLCs generated by pretrained CNNs in an explicit way to investigate the relations/associations between these HLCs and a (small) set of Ekman's emotional classes. As a proof-of-concept, we first propose a linear admixture model for modeling these relations, and the resulting computational framework allows us "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02751","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1705.02751","created_at":"2026-05-18T00:44:53.744817+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.02751v1","created_at":"2026-05-18T00:44:53.744817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02751","created_at":"2026-05-18T00:44:53.744817+00:00"},{"alias_kind":"pith_short_12","alias_value":"IG2UZGZ37M3Q","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"IG2UZGZ37M3QJIJV","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"IG2UZGZ3","created_at":"2026-05-18T12:31:21.493067+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/IG2UZGZ37M3QJIJVIPUPJYKIB6","json":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6.json","graph_json":"https://pith.science/api/pith-number/IG2UZGZ37M3QJIJVIPUPJYKIB6/graph.json","events_json":"https://pith.science/api/pith-number/IG2UZGZ37M3QJIJVIPUPJYKIB6/events.json","paper":"https://pith.science/paper/IG2UZGZ3"},"agent_actions":{"view_html":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6","download_json":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6.json","view_paper":"https://pith.science/paper/IG2UZGZ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.02751&json=true","fetch_graph":"https://pith.science/api/pith-number/IG2UZGZ37M3QJIJVIPUPJYKIB6/graph.json","fetch_events":"https://pith.science/api/pith-number/IG2UZGZ37M3QJIJVIPUPJYKIB6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6/action/storage_attestation","attest_author":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6/action/author_attestation","sign_citation":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6/action/citation_signature","submit_replication":"https://pith.science/pith/IG2UZGZ37M3QJIJVIPUPJYKIB6/action/replication_record"}},"created_at":"2026-05-18T00:44:53.744817+00:00","updated_at":"2026-05-18T00:44:53.744817+00:00"}