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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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorZhenqi He →
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
- Linked via arxiv authorZiqi Jiang →
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
- Linked via arxiv authorYuanpei Liu →
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
- Linked via arxiv authorYanghao Wang →
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
- Linked via arxiv authorTeng Wang →
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
- Linked via arxiv authorTianlong Chen →
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval