  Mixed-Modal Image Retrieval (MMIR) as a flexible search paradigm has
attracted wide attention. However, previous approaches always achieve limited
performance, due to two critical factors are seriously overlooked. 1) The
contribution of image and text modalities is different, but incorrectly treated
equally. 2) There exist inherent labeling noises in describing users'
intentions with text in web datasets from diverse real-world scenarios, giving
rise to overfitting. We propose a Dynamic Weighted Combiner (DWC) to tackle the
above challenges, which includes three merits. First, we propose an Editable
Modality De-equalizer (EMD) by taking into account the contribution disparity
between modalities, containing two modality feature editors and an adaptive
weighted combiner. Second, to alleviate labeling noises and data bias, we
propose a dynamic soft-similarity label generator (SSG) to implicitly improve
noisy supervision. Finally, to bridge modality gaps and facilitate similarity
learning, we propose a CLIP-based mutual enhancement module alternately trained
by a mixed-modality contrastive loss. Extensive experiments verify that our
proposed model significantly outperforms state-of-the-art methods on real-world
datasets. The source code is available at
\url{https://github.com/fuxianghuang1/DWC}.
