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

• URL: http://arxiv.org/abs/2607.02284v1
• Date: Thu, 02 Jul 2026 15:02:45 GMT
• Title: FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
• Abstract summary: ZS-CIR aims to retrieve a target image by editing a reference image with a natural-language instruction, without relying on domain-specific triplets.<n>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.<n>We propose FlowCIR, that casts ZS-CIR as conditional semantic transport between reference and target embeddings.
• Score: 15.55344484616571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 embeddings. Leveraging \emph{conditional flow matching}, our model learns a lightweight transport field that maps the instruction representation toward a target-aligned query embedding conditioned on the reference image. Since FlowCIR operates on pre-extracted VLM embeddings and trains only a small transport module without updating the image or text encoder, it offers a computationally efficient training protocol compared with prior textual-inversion-based approaches. The resulting framework is training-efficient, requiring roughly $10\times$ fewer training resources than prior textual-inversion-based approaches. We further identify negation and removal as a major failure mode of VLM-based composition. To address this, we propose an inference-only Multi-Negative Steering strategy that steers a negation-containing relative instruction away from its negated semantics, mitigating the limited negation handling of VLMs and improving robustness on negation-heavy queries. Extensive experiments on standard CIR benchmarks demonstrate that FlowCIR achieves strong and competitive performance compared with recent ZS-CIR methods.

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