[AAAI'26] INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval

Zhiwei Chen1, Yupeng Hu1*, Zhiheng Fu1, Zixu Li1, Jiale Huang1, Qinlei Huang1, Yinwei Wei1,
1School of Software, Shandong University
*Corresponding author.
Paper Complete source code

Abstract

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Noise Types and Decision Boundaries in CIR

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(a) shows typical Modality-inherent Noise in CIR. (b) reveals Cross-modal Correspondence Noise. (c) presents two cases: left successfully retrieves target with low confidence (50%), while right fails with high confidence (70%), illustrating varying decision boundaries in retrievals.


Framework: Invariance and discrimiNaTion-awarE Noise neTwork (INTENT)

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The framework of our proposed INTENT. We designed (a) Visual Invariant Composition and (b) Bi-Objective Discriminative Learning, to mitigate modality-inherent noise and cross-modal correspondence noise, respectively.

Experiment

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Performance comparison on the FashionIQ validation set in terms of R@K(%). The best and second-best results are highlighted in bold and underline, respectively.


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Performance comparison on the CIRR test set in terms of R@K(%) and Rsub@K(%). The best and second-best results are highlighted in bold and underline, respectively.

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The ablation study for modules of INTENT.

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The ablation study for loss functions of INTENT.


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Case Study on CIRR and FashionIQ.

BibTeX


        @inproceedings{INTENT,
            title={INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval},
            author={Chen, Zhiwei and Hu, Yupeng and Fu, Zhiheng and Li, Zixu and Huang, Jiale and Huang, Qinlei and Wei, Yinwei},
            booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
            year={2026}
        }