FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image by editing a reference image with a natural-language instruction, without relying on domain-specific annotated triplets. Most existing ZS-CIR methods rely on textual inversion to translate the reference image into pseudo-text tokens and then compose them with the instruction via simple concatenation in the text space, which can be lossy and brittle for fine-grained semantics. In this work, we propose a new paradigm, namely FlowCIR, that casts ZS-CIR as conditional semantic transport between reference and target embeddi
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- LinkedLinked via arxiv author · 85%Zhenqi He →
“FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval”
- LinkedLinked via arxiv author · 85%Ziqi Jiang →
“FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval”
- LinkedLinked via arxiv author · 85%Yuanpei Liu →
“FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval”
- LinkedLinked via arxiv author · 85%Yanghao Wang →
“FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval”
- LinkedLinked via arxiv author · 85%Teng Wang →
“FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval”
- LinkedLinked via arxiv author · 85%Tianlong Chen →
“FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval”