Flow-CIR: Generative Feature Rectification for Noise-Robust Composed Image Retrieval

Composed image retrieval aims to retrieve a target image according to a reference image and a textual modification. Existing methods usually assume that training triplets are correctly matched, while real-world annotations may contain noisy triplet correspondence caused by subjective modification texts, ambiguous visual changes, and imperfect target selection. Such noise is especially challenging because a suspicious triplet is not always completely wrong; it may still preserve partial semantic cues that are useful for learning fine-grained visual transformations. Most noise-robust CIR methods mainly identify, suppress, or down-weight unreliable triplets, which reduces harmful supervision but may also discard informative signals from hard samples. In this paper, we propose Flow-CIR, a feature-level generative rectification framework for noise-robust composed image retrieval. Instead of simply forgetting suspicious triplets, Flow-CIR learns a conditional semantic vector field from reliable triplets and uses it to generate pseudo-target features for suspicious samples. To avoid over-correction, the rectified target feature is softly mixed with the observed target representation and is applied only after a warm-up stage. Since rectification is performed in the compact feature space, Flow-CIR introduces no additional inference cost. Experiments on FashionIQ and CIRR under different noise ratios demonstrate the robustness and effectiveness of the proposed framework.