CVPR 2023 - SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency

In this episode we discuss SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency by Yang Liu, Yao Zhang, Yixin Wang, Yang Zhang, Jiang Tian, Zhongchao Shi, Jianping Fan, Zhiqiang He. The paper proposes SAlient Point-based DETR (SAP-DETR), a new approach to object detection that treats it as a transformation from salient points to instance objects. SAP-DETR addresses the issue of centralizing reference points that can deteriorate queries' saliency and confuse detectors. By explicitly initializing a query-specific reference point for each object query and gradually aggregating them into an instance object, SAP-DETR can effectively bridge the gap between salient points and query-based Transformer detector with a significant convergency speed. The method achieves competitive performance and stably promotes state-of-the-art approaches.