For all parameter settings, see Appendix B.2. The code of our CRCL will be released on GitHub.

These works attempt to leverage the memorization effect of DNNs [25] to gradually distinguish the noisy image-text pairs for robust learning in a co-teaching manner.

Image-text14 retrieval aims to retrieve the most relevant text from candidate images, or vice versa.

Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge in image-text matching lies in language bias, where models predominantly rely on language priors and neglect to adequately consider the visual content. We thus present Multimodal ASsociation Score (MASS), a framework that reduces the reliance on language priors for better visual accuracy in image-text matching problems. It can be seamlessly incorporated into existing visual-language models without necessitating additional training. Our experiments have shown that MASS effectively lessens language bias without losing an understanding of linguistic compositionality. Overall, MASS offers a promising solution for enhancing image-text matching performance in visual-language models.