{"id":"395a1cd2-ca3d-4a60-81c7-44670f870fd9","arxiv_id":"2504.12029","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"A semi-supervised framework with pairwise similarity transfer improves discriminative object placement on novel categories by leveraging unlabeled Open Images data.","lead":"This paper proposes a semi-supervised training framework for object placement, using both a small labeled dataset and a large unlabeled dataset. It shows improved placement accuracy on novel object categories, which matters for image compositing tools that need to handle arbitrary objects.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-16T12:39:21.728077+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}