{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:L3LVY2NQLLROHV2SHSG4HC7YAF","short_pith_number":"pith:L3LVY2NQ","schema_version":"1.0","canonical_sha256":"5ed75c69b05ae2e3d7523c8dc38bf801795b6b34e4e13a5eafe97ab1b9d492ac","source":{"kind":"arxiv","id":"1711.01062","version":1},"attestation_state":"computed","paper":{"title":"Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guyue Zhang, Hengduo Li, Jun Liu, Yirui Wu, Yuan Gao","submitted_at":"2017-11-03T08:52:42Z","abstract_excerpt":"With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information. To the best of our knowledge, this is the first attempt to utilize LSTM structure for RGB-D bas"},"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":"1711.01062","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T08:52:42Z","cross_cats_sorted":[],"title_canon_sha256":"fb289b102c799ade9fc23463c4bec92231975bc85990de66fac5d4d27ad4aae5","abstract_canon_sha256":"acc2583589cf7077089021bbff9be6a071d05ea9845f42e078487a88f5c6c486"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:24.166964Z","signature_b64":"eGKJV0DvC1wUB/dqDqJ4Ksw5leRt1taGmQcqNbFO8TLAfHoqQZ1b4avqX5YvNxhI9EDRPRtj8jh3KgHENs0rBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ed75c69b05ae2e3d7523c8dc38bf801795b6b34e4e13a5eafe97ab1b9d492ac","last_reissued_at":"2026-05-18T00:31:24.166408Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:24.166408Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guyue Zhang, Hengduo Li, Jun Liu, Yirui Wu, Yuan Gao","submitted_at":"2017-11-03T08:52:42Z","abstract_excerpt":"With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information. To the best of our knowledge, this is the first attempt to utilize LSTM structure for RGB-D bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01062","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":"1711.01062","created_at":"2026-05-18T00:31:24.166487+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.01062v1","created_at":"2026-05-18T00:31:24.166487+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01062","created_at":"2026-05-18T00:31:24.166487+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3LVY2NQLLRO","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3LVY2NQLLROHV2S","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3LVY2NQ","created_at":"2026-05-18T12:31:28.150371+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/L3LVY2NQLLROHV2SHSG4HC7YAF","json":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF.json","graph_json":"https://pith.science/api/pith-number/L3LVY2NQLLROHV2SHSG4HC7YAF/graph.json","events_json":"https://pith.science/api/pith-number/L3LVY2NQLLROHV2SHSG4HC7YAF/events.json","paper":"https://pith.science/paper/L3LVY2NQ"},"agent_actions":{"view_html":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF","download_json":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF.json","view_paper":"https://pith.science/paper/L3LVY2NQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.01062&json=true","fetch_graph":"https://pith.science/api/pith-number/L3LVY2NQLLROHV2SHSG4HC7YAF/graph.json","fetch_events":"https://pith.science/api/pith-number/L3LVY2NQLLROHV2SHSG4HC7YAF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF/action/storage_attestation","attest_author":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF/action/author_attestation","sign_citation":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF/action/citation_signature","submit_replication":"https://pith.science/pith/L3LVY2NQLLROHV2SHSG4HC7YAF/action/replication_record"}},"created_at":"2026-05-18T00:31:24.166487+00:00","updated_at":"2026-05-18T00:31:24.166487+00:00"}