Single-stage zero-shot object detection network based on CLIP and pseudo-labeling

The detection of unknown objects is a challenging task in computer vision because, although there are diverse real-world detection object categories, existing object-detection training sets cover a limited number of object categories . Most existing approaches use two-stage networks to improve a model's ability to characterize objects of unknown classes, which leads to slow inference. To address this issue, we proposed a single-stage unknown object detection method based on the contrastive language-image pre-training (CLIP) model and pseudo-labelling, called CLIP-YOLO. First, a visual language embedding alignment method is introduced and a channel-grouped enhanced coordinate attention module is embedded into a YOLO-series detection head and feature-enhancing component, to improve the model's ability to characterize and detect unknown category objects. Second, the pseudo-labelling generation is optimized based on the CLIP model to expand the diversity of the training set and enhance the ability to cover unknown object categories. We validated this method on four challenging datasets: MSCOCO, ILSVRC, Visual Genome, and PASCAL VOC. The results show that our method can achieve higher accuracy and faster speed, so as to obtain better performance of unknown object detection. The source code is available at https://github.com/BJUTsipl/CLIP-YOLO.