Composed Image Retrieval (CIR) retrieves target images based on the composition of a reference image and a textual modification. Existing CIR approaches commonly assume that training triplets are perfectly aligned, yet large-scale web-sourced data inevitably contain noise. Such noise may originate from the reference image, the modification text, or the target image. However, current approaches lack mechanisms to distinguish and handle these different types of noise. To address this problem, we propose a novel noisy correspondence decomposition framework that categorizes noisy triplets according to different noise types. Specifically, it identifies text noise by calculating the visual alignment between reference and target images, and detects reference-image noise by comparing fused features against the reference and target representations. Building on this framework, we introduce a multi-path composed image retrieval (MPCIR) method that routes triplets along different learning paths based on their noise type, enabling the model to extract reliable supervision from partially misaligned data. Finally, to bridge the gap between clean benchmarks and real-world data, we introduce a reconstruction framework that transforms existing annotations into realistic datasets. This yields Real-CIRR and Real-FashionIQ, two new datasets that reflect naturally occurring noisy correspondences in real-world web search scenarios. Extensive experiments on four CIR datasets show that MPCIR delivers strong robustness under both synthetic and real-world noise.
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