Multi-modal Prompts with Feature Decoupling for Open-Vocabulary Object Detection

Open-vocabulary object detection aims to acquire the ability to recognize novel categories through text description using data of limited categories for training. The Prompt serves as a template to assist in the construction of textual descriptions for categories. With the development of open-vocabulary object detection, multi-modal prompts with better performance have emerged. However, existing multi-modal prompts fail to align the context and object components across different modalities during the construction. To address the issue, we propose an open-vocabulary object detection framework based on multi-modal prompts with feature decoupling. The framework consists of two modules, the construction of Multi-modal Prompts with Feature Decoupling (MPFD) and the visual Region Expansion (RE). During prompts constructing, the MPFD decouples the object and context components from the visual embeddings and then performs multi-modal fusion with the corresponding parts of the text embeddings respectively. The RE incorporates additional context information into the visual embeddings to enhance the discriminative ability of the prompts. Sufficient experiments have demonstrated that feature decoupling multi-modal prompts can effectively improve the performance of open-vocabulary object detection models.