SOURCE-LINKED INTELLIGENCE
MulVec: Fine-Grained Role-Aware Matching for Training-Free Zero-Shot Composed Image Retrieval
Training-free zero-shot composed image retrieval finds a target image in a gallery from a reference image and a text edit without learning from task-specific image triplets. Existing methods typically describe the target as a whole and match this description with a global image representation. This global matching can mix different semantic cues and lose fine- grained details. We propose MULVEC, a role-aware method whose compiler produces a structured query record that is mapped to four retrieval roles: Global describes the full target, Desired states what should appear, Preserve states what s
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T02:24:41.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.