ClipMix for Domain Generalization
An-An Liu, H.Z. Li, Wenhui Li, Dan Song, Hongshuo Tian, Lanjun Wang · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Domain Generalization (DG) is a growing field in machine learning that aims to train the model across multiple source domains, thereby enabling effective generalization to new, unseen target domains. Recent studies suggest that data augmentation, which enhances the diversity of the source domain, might be a promising solution to address this task. Current data augmentation methods use random fusion coefficients or local regional fusion, which cannot adaptively design the weights based on data, or preserve the integrity of original semantics. Inspired by the pre-trained model CLIP, which contains extensive multimodal knowledge, we propose ClipMix to address these limitations. Firstly, we use the CLIP model as the external knowledge to adaptively evaluate the alignment between images and their labels, using this alignment to assess the complexity of learning each image and guide adaptive augmentation. Secondly, we implement a label shift mechanism to dynamically assign soft labels to fused images, helping the model focus on hard-to-learn patterns and also gather domain-agnostic representation. Furthermore, we enhance the diversity of fused images at both the pixel and feature levels. Experimental results across sixteen domains from four databases verify the effectiveness of our method.