{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NNMH6SCUZXPKBS4W4BOARUO2VS","short_pith_number":"pith:NNMH6SCU","schema_version":"1.0","canonical_sha256":"6b587f4854cddea0cb96e05c08d1daacb9493bf7a075e7859c49d198969a18d9","source":{"kind":"arxiv","id":"1803.01529","version":1},"attestation_state":"computed","paper":{"title":"LSTD: A Low-Shot Transfer Detector for Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guoyou Wang, Hao Chen, Yali Wang, Yu Qiao","submitted_at":"2018-03-05T07:30:58Z","abstract_excerpt":"Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate trans"},"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":"1803.01529","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-05T07:30:58Z","cross_cats_sorted":[],"title_canon_sha256":"2466b3ab78f38526b087702712b6c30b38d899d97c18d15b7697a5e720c97c2a","abstract_canon_sha256":"256b55d0d7db14ea6268ccef848214765e5a0af0a2d6576d80c08e354b6aca01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:00.372159Z","signature_b64":"OC1gSM0Nw6NpYNZKtxzhIJ7S2ixBMZ4I34HNhGSc4ZsYacntnRgG6cmhQNkmH1WtxDcGRu7FhY/GszSBdR43DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b587f4854cddea0cb96e05c08d1daacb9493bf7a075e7859c49d198969a18d9","last_reissued_at":"2026-05-18T00:22:00.371574Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:00.371574Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LSTD: A Low-Shot Transfer Detector for Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guoyou Wang, Hao Chen, Yali Wang, Yu Qiao","submitted_at":"2018-03-05T07:30:58Z","abstract_excerpt":"Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate trans"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01529","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":"1803.01529","created_at":"2026-05-18T00:22:00.371665+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.01529v1","created_at":"2026-05-18T00:22:00.371665+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01529","created_at":"2026-05-18T00:22:00.371665+00:00"},{"alias_kind":"pith_short_12","alias_value":"NNMH6SCUZXPK","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NNMH6SCUZXPKBS4W","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NNMH6SCU","created_at":"2026-05-18T12:32:40.477152+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/NNMH6SCUZXPKBS4W4BOARUO2VS","json":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS.json","graph_json":"https://pith.science/api/pith-number/NNMH6SCUZXPKBS4W4BOARUO2VS/graph.json","events_json":"https://pith.science/api/pith-number/NNMH6SCUZXPKBS4W4BOARUO2VS/events.json","paper":"https://pith.science/paper/NNMH6SCU"},"agent_actions":{"view_html":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS","download_json":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS.json","view_paper":"https://pith.science/paper/NNMH6SCU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.01529&json=true","fetch_graph":"https://pith.science/api/pith-number/NNMH6SCUZXPKBS4W4BOARUO2VS/graph.json","fetch_events":"https://pith.science/api/pith-number/NNMH6SCUZXPKBS4W4BOARUO2VS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS/action/storage_attestation","attest_author":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS/action/author_attestation","sign_citation":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS/action/citation_signature","submit_replication":"https://pith.science/pith/NNMH6SCUZXPKBS4W4BOARUO2VS/action/replication_record"}},"created_at":"2026-05-18T00:22:00.371665+00:00","updated_at":"2026-05-18T00:22:00.371665+00:00"}