An enhanced domain generalization method for object detection based on text guided feature disentanglement

The application scenarios of object detection models are constantly changing, due to the alternation of day and night and weather changes. Detector often suffers from the scarcity of training sets on potential domains. Recently, this challenge known as domain shift has been relieved by single domain generalization (SDG). To further generalize towards multiple unseen domains, this paper proposes a detector that uses text semantic gaps to enhance scene diversity and utilizes feature disentangling to extract domain-invariant features from different scenes, thereby improving detection accuracy. Firstly, random semantic augmentation (RSA) is adopted leveraging the text modality to capture semantically generalized representations, thereby augmenting the diversity of domain related information. Second, by broadening the decision boundary between domain-invariant and domain-specific features, feature disentangling (FD) branches are applied to improve the detector's object-background differentiation. Additionally, a cross modality alignment (CMA) is performed by estimating the relevances between domain-specific features and textual domain prompts. Experimental results show the proposed detector has excellent performance among existing baselines on diverse weather conditions, such as rainy, foggy and night rainy, which also confirms the enhanced generalization ability on multiple unseen domains.