Zhao Gao, Mina Metias, Mohamed Noordeen Alaudeen, Bassel Saleh · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26661 · open-access PDF
Composed Image Retrieval (CIR) は、自然言語で修正された参照画像に匹敵する製品を取得します──電子商取引の検索のためのコア機能です。既存のトレーニングフリーメソッドは、数値を介して CLIP 埋め込みを統合しますが、基本的な制限を特定します:ユーザのテキストの変更は属性不足です──「短い袖で赤くする」と書くユーザは、何を変更するかを指定しますが、何を保持するかを指定します(花のパターン、V-ネック、フィット-and-flareシルエット)。
Machine translation (Meta M2M100); the English original follows.
Composed Image Retrieval (CIR) retrieves products matching a reference image modified by natural language---a core capability for eCommerce search. Existing training-free methods fuse CLIP embeddings via arithmetic, but we identify a fundamental limitation: user text modifications are attribute-sparse---a user who types ``make it red with shorter sleeves'' specifies what to change but not what to keep (the floral pattern, the V-neck, the fit-and-flare silhouette). We introduce language-space fusion: a Vision-Language Model (VLM) jointly interprets the reference image and modification to genera
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