TRUST: Semantic Relation-Aware Robust Learning Framework for Composed Image Retrieval

Composed Image Retrieval enables flexible image search using a reference image and a modification text, yet suffers from Noisy Triplet Correspondence caused by mismatched triplet components. Existing methods typically discriminate noisy samples via query-target-level signals, while overlooking the intrinsic semantic relations among the reference, modification, and target. Specifically, noisy triplets may still appear reliable when query-target similarity is dominated by unchanged visual content or when the composed query obscures invalid component-target relations. To address these limitations, we propose TRUST: a Semantic Relation-Aware Robust Learning framework, which comprises two coherent and synergistic processes. Transformation-Aware Reliability Estimation first measures continuous triplet reliability on the unit hypersphere by jointly modeling Reference-Anchored Transformation Agreement and Target-oriented Query Decoupling. Then, Reliability-Guided Asymmetric Optimization uses the estimated reliability to weight bidirectional ranking, while shaping the spherical representation space through transformation alignment and target uniformity. We further introduce eXtreme Noisy Triplet Correspondence as a more challenging setting. Experiments on Fashion-IQ and CIRR show that TRUST achieves state-of-the-art performance, with substantial advantages at high noise rates.