FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval

Zero-shot composed image retrieval (ZS-CIR) aims to re-trieve a target image by editing a reference image with a natural-languageinstruction, without relying on domain-specific annotated triplets. Mostexisting ZS-CIR methods rely on textual inversion to translate the ref-erence image into pseudo-text tokens and then compose them with theinstruction via simple concatenation in the text space, which can belossy and brittle for fine-grained semantics. In this work, we propose anew paradigm, namely FlowCIR, that casts ZS-CIR as conditionalsemantic transport between reference and target embeddings. Leverag-ing conditional flow matching, our model learns a lightweight transportfield that maps the instruction representation toward a target-alignedquery embedding conditioned on the reference image. Since FlowCIRoperates on pre-extracted VLM embeddings and trains only a smalltransport module without updating the image or text encoder, it of-fers a computationally efficient training protocol compared with priortextual-inversion-based approaches. We further identify negation and re-moval as a major failure mode of VLM-based composition. To addressthis, we propose an inference-only Multi-Negative Steering strategy thatsteers a negation-containing relative instruction away from its negatedsemantics, mitigating the limited negation handling of VLMs and im-proving robustness on negation-heavy queries. Extensive experiments onstandard CIR benchmarks demonstrate that FlowCIR achieves strongand competitive performance compared with recent ZS-CIR methods.Project page: https://hkust-longgroup.github.io/FlowCIR