FRAME: 属性読み出しによる因子分解型検索を用いたオブジェクト中心シーン記憶
FRAME: Factored Retrieval via Attribute Readouts for Object-Centric Scene Memory
言語指示でロボットが物体を検索する際、複数の永続的属性（カテゴリ、材質、サイズなど）を組み合わせた検索を可能にするFRAMEを提案。属性ごとの証拠を学習し、軽量な行列計算で高精度なマルチ属性検索を実現した。
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著者: Woosang Jeon, Sanghyeok Choi, Minwoo Kim, Taehyun Jung, Taehyeong Kim
分類: cs.CV, cs.RO
原文アブストラクト
Language-guided robots need persistent scene memories to follow instructions, revisit objects, and resolve references to objects encountered over time. While much of language-guided scene-memory retrieval has emphasized spatial or relational references, many everyday object references specify objects by multiple persistent attributes, such as category, material, size, or surface appearance. We formalize this problem as attribute-compositional retrieval, where a fixed object-centric scene memory is queried with natural language to retrieve the object satisfying the requested attributes. To investigate this capability directly, we introduce a controlled evaluation protocol with fixed scene memories and attribute-defined targets, separating retrieval from perception and annotation ambiguities. We then propose FRAME, which turns language into query-relevant attribute weights, uses learned readouts to estimate per-attribute evidence from object embeddings, and ranks objects by aggregating this evidence according to the query. Across held-out scenes and object assets, FRAME outperforms representative scene-memory retrieval baselines while reducing post-decomposition object scoring to lightweight matrix-vector computation. These results position attribute-compositional retrieval as a complementary scene-memory capability for language-guided robots, showing that persistent object attributes can be exposed as composable evidence for accurate and efficient multi-attribute retrieval.