{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FENQ7GCJZMEP4R6AW7RVX6AFQM","short_pith_number":"pith:FENQ7GCJ","schema_version":"1.0","canonical_sha256":"291b0f9849cb08fe47c0b7e35bf8058306bd8045c5062a1f98d5e47e7ddd9313","source":{"kind":"arxiv","id":"2607.18827","version":1},"attestation_state":"computed","paper":{"title":"Open-Vocabulary Gaze Object Prediction: Benchmark and Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binglu Wang, Guangyu Guo, Sensen Niu, Ying Chen","submitted_at":"2026-07-21T08:03:02Z","abstract_excerpt":"Gaze Object Prediction (GOP) aims to localize and recognize the objects humans attend to, a task crucial for understanding human-centric interactions. However, existing methods are typically trained under a closed-vocabulary paradigm with a fixed label space and evaluated on scene-specific datasets, limiting their applicability to real-world scenarios where gaze targets often follow a long-tail distribution or belong to unseen categories. To address this gap, we introduce Diverse Scenes for Gaze object prediction (DiSG), a benchmark containing 86 in-the-wild categories that facilitates the eva"},"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":"2607.18827","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T08:03:02Z","cross_cats_sorted":[],"title_canon_sha256":"31b681d862bc5ffb8af48f4c6f33bb07a8ec6deb084adfa84932b2afb50927b1","abstract_canon_sha256":"1d9c133c74288b80221d71120e4f9399bd442737c15e4924a11b3743ed06ec57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T01:23:14.711842Z","signature_b64":"8BFFJQ7XnUZaEBfbdBtxb6ouVR2nPuGN8few2cItZ2BaObmgN5UM7Wf/kOzjOl+J5Z0IKAswg8btbLDpO+FBBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"291b0f9849cb08fe47c0b7e35bf8058306bd8045c5062a1f98d5e47e7ddd9313","last_reissued_at":"2026-07-22T01:23:14.710950Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T01:23:14.710950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Open-Vocabulary Gaze Object Prediction: Benchmark and Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binglu Wang, Guangyu Guo, Sensen Niu, Ying Chen","submitted_at":"2026-07-21T08:03:02Z","abstract_excerpt":"Gaze Object Prediction (GOP) aims to localize and recognize the objects humans attend to, a task crucial for understanding human-centric interactions. However, existing methods are typically trained under a closed-vocabulary paradigm with a fixed label space and evaluated on scene-specific datasets, limiting their applicability to real-world scenarios where gaze targets often follow a long-tail distribution or belong to unseen categories. To address this gap, we introduce Diverse Scenes for Gaze object prediction (DiSG), a benchmark containing 86 in-the-wild categories that facilitates the eva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18827","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/2607.18827/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":"2607.18827","created_at":"2026-07-22T01:23:14.711408+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18827v1","created_at":"2026-07-22T01:23:14.711408+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18827","created_at":"2026-07-22T01:23:14.711408+00:00"},{"alias_kind":"pith_short_12","alias_value":"FENQ7GCJZMEP","created_at":"2026-07-22T01:23:14.711408+00:00"},{"alias_kind":"pith_short_16","alias_value":"FENQ7GCJZMEP4R6A","created_at":"2026-07-22T01:23:14.711408+00:00"},{"alias_kind":"pith_short_8","alias_value":"FENQ7GCJ","created_at":"2026-07-22T01:23:14.711408+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/FENQ7GCJZMEP4R6AW7RVX6AFQM","json":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM.json","graph_json":"https://pith.science/api/pith-number/FENQ7GCJZMEP4R6AW7RVX6AFQM/graph.json","events_json":"https://pith.science/api/pith-number/FENQ7GCJZMEP4R6AW7RVX6AFQM/events.json","paper":"https://pith.science/paper/FENQ7GCJ"},"agent_actions":{"view_html":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM","download_json":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM.json","view_paper":"https://pith.science/paper/FENQ7GCJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18827&json=true","fetch_graph":"https://pith.science/api/pith-number/FENQ7GCJZMEP4R6AW7RVX6AFQM/graph.json","fetch_events":"https://pith.science/api/pith-number/FENQ7GCJZMEP4R6AW7RVX6AFQM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM/action/storage_attestation","attest_author":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM/action/author_attestation","sign_citation":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM/action/citation_signature","submit_replication":"https://pith.science/pith/FENQ7GCJZMEP4R6AW7RVX6AFQM/action/replication_record"}},"created_at":"2026-07-22T01:23:14.711408+00:00","updated_at":"2026-07-22T01:23:14.711408+00:00"}