Enhancing cross-domain generalization by fusing language-guided feature remapping

Abstract

Domain generalization refers to training a model with annotated source domain data and making it generalize to various unseen target domains. It has been extensively studied in classification, but remains challenging in object detection. Existing domain generalization object detection methods mainly rely on generative or adversarial data augmentation, which increases the complexity of training. Recently, vision-language models (VLMs), such as CLIP, have demonstrated strong cross-modal alignment capabilities, showing potential for enhancing domain generalization. On this basis, the paper proposes a language-guided feature remapping method, which leverages VLMs to augment sample features and improve the generalization performance of regular models. In detail, we first construct a teacher-student network structure. Then, we introduce a feature remapping module that remaps sample features in both local and global spatial dimensions to improve the distribution of feature representations. Concurrently, we design domain prompts and class prompts to guide the sample features to remap into a more generalized and universal feature space. Finally, we establish a knowledge distillation structure to facilitate knowledge transfer between teacher and student networks, enhancing the domain generalization ability of the student network. Multiple experimental results demonstrate the superiority of our proposed method.