New benchmark exposes widespread attribute-object binding failures in multimodal retrievers
Netanel Y. Tamir · hf · 2026-10-19
A team from OriginAI, NVIDIA, and Tel Aviv University releases a benchmark exposing widespread attribute-object binding failures in multimodal retrievers: given multi-object queries (e.g., 'glowing giraffe, tie-dye submarine, golden cannon'), mainstream models frequently mismatch attributes across objects. The paper further shows that fine-tuning on synthetic text improves cross-modal binding without any image supervision. Project page is public.
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