CAKE: Category Aware Knowledge Extraction for Open-Vocabulary Object Detection

Open vocabulary object detection (OVOD) task aims to detect objects of novel categories beyond the base categories in the training set. To this end, the detector needs to access image-text pairs containing rich semantic information or the visual language pre-trained model (VLM) learned on them. Recent OVOD methods rely on knowledge distillation from VLMs. However, there are two main problems in current methods: (1) Current knowledge distillation frameworks fail to take advantage of the global category information of VLMs and thus fail to learn category-specific knowledge. (2) Due to the overfitting phenomenon of base categories during training, current OVOD networks generally have the problem of suppressing novel categories as background. To address these two problems, we propose a Category Aware Knowledge Extraction framework (CAKE), which consists of a Category-Specific Knowledge Distillation branch (CSKD) and a Category Generalization Region Proposal Network (CG-RPN). CSKD can more fully extract category-strong related information through category-specific distillation, and it is also conducive to filtering the exclusion problem between individuals of the same category; in this process, the model constructs a category-specific feature set to maintain high-quality category features. CG-RPN leverages the guidance of feature set to adjust the confidence scores of region proposals, thereby mining proposals that potentially contain novel categories of objects. Extensive experiments show that our method can plug and play well with many existing methods and significantly improve their detection performance. Moreover, our CAKE framework can reach the-state-of-the-art performance on OV-COCO and OV-LVIS datasets.