CVPR 2023 - DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection

In this episode we discuss DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection by Zongheng Tang, Yifan Sun, Si Liu, Yi Yang. The paper proposes a method for cross-domain weakly supervised object detection (CDWSOD) by adapting the detector from source to target domain through weak supervision using DETR (transformers-based object detection model). The proposed method, DETR-GA, simultaneously makes "instance-level + image-level" predictions and utilizes "strong + weak" supervisions. The method uses query-based aggregation that helps in locating corresponding positions, excluding distractions from non-relevant regions, and making strong and weak supervision mutually benefit each other for domain alignment. Extensive experiments show that DETR-GA significantly improves cross-domain detection accuracy and advances the state-of-the-art.