{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Y2QTCGIP7RHRMGEZN2QK4IECE3","short_pith_number":"pith:Y2QTCGIP","schema_version":"1.0","canonical_sha256":"c6a131190ffc4f1618996ea0ae208226e5386db05d4e29e1b8196591a32deae5","source":{"kind":"arxiv","id":"2110.01774","version":2},"attestation_state":"computed","paper":{"title":"HighlightMe: Detecting Highlights from Human-Centric Videos","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dinesh Manocha, Gang Wu, Stefano Petrangeli, Uttaran Bhattacharya, Viswanathan Swaminathan","submitted_at":"2021-10-05T01:18:15Z","abstract_excerpt":"We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of "},"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":"2110.01774","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-10-05T01:18:15Z","cross_cats_sorted":[],"title_canon_sha256":"4110fee06dac54f40ab31049084fb34cf00cfe66c10d980ce6bd5d87a16985d9","abstract_canon_sha256":"5f878f80f7ccc15ccb1d185f9239104d3fdc45aef88f91ed87bab1d8a4bc3e5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:17.560247Z","signature_b64":"zMpFR7bOf47xt/+lh3qWx12sllORdSEmoyvtFplHaSB1ndakE3Aa+/bYmEXe7/T6mTGGjptPgt9ASCH8BJb3Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6a131190ffc4f1618996ea0ae208226e5386db05d4e29e1b8196591a32deae5","last_reissued_at":"2026-07-05T09:39:17.559783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:17.559783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HighlightMe: Detecting Highlights from Human-Centric Videos","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dinesh Manocha, Gang Wu, Stefano Petrangeli, Uttaran Bhattacharya, Viswanathan Swaminathan","submitted_at":"2021-10-05T01:18:15Z","abstract_excerpt":"We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.01774","kind":"arxiv","version":2},"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/2110.01774/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":"2110.01774","created_at":"2026-07-05T09:39:17.559835+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.01774v2","created_at":"2026-07-05T09:39:17.559835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.01774","created_at":"2026-07-05T09:39:17.559835+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2QTCGIP7RHR","created_at":"2026-07-05T09:39:17.559835+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2QTCGIP7RHRMGEZ","created_at":"2026-07-05T09:39:17.559835+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2QTCGIP","created_at":"2026-07-05T09:39:17.559835+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/Y2QTCGIP7RHRMGEZN2QK4IECE3","json":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3.json","graph_json":"https://pith.science/api/pith-number/Y2QTCGIP7RHRMGEZN2QK4IECE3/graph.json","events_json":"https://pith.science/api/pith-number/Y2QTCGIP7RHRMGEZN2QK4IECE3/events.json","paper":"https://pith.science/paper/Y2QTCGIP"},"agent_actions":{"view_html":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3","download_json":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3.json","view_paper":"https://pith.science/paper/Y2QTCGIP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.01774&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2QTCGIP7RHRMGEZN2QK4IECE3/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2QTCGIP7RHRMGEZN2QK4IECE3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3/action/storage_attestation","attest_author":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3/action/author_attestation","sign_citation":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3/action/citation_signature","submit_replication":"https://pith.science/pith/Y2QTCGIP7RHRMGEZN2QK4IECE3/action/replication_record"}},"created_at":"2026-07-05T09:39:17.559835+00:00","updated_at":"2026-07-05T09:39:17.559835+00:00"}