Cross-view consistent teacher for source-free object detection
TL;DR
Experimental results show that the proposed SFDA object detection framework for the YOLO family of single-stage detectors consistently outperforms the baseline on multiple cross-domain detection tasks, demonstrating its effectiveness and good generalization ability under the source-free setting.
Abstract
Source-free domain adaptation (SFDA) aims to adapt pre-trained source models to new target domains without requiring access to any source domain data, thereby addressing privacy and efficiency concerns. Existing SFDA methods for object detection primarily follow a teacher–student self-training paradigm; however, their performance is often limited by noisy pseudo-labels. To address this issue, this paper proposes an SFDA object detection framework for the YOLO family of single-stage detectors. First, a weak–strong pseudo-label consistency filtering strategy is designed to remove unreliable pseudo-labels by exploiting the prediction consistency across different augmented views. Second, a multiscale object-level contrastive learning mechanism is introduced to extract object-level features at multiple feature scales, thereby enhancing the consistency and discriminability of object representations across different views and scales through supervised contrastive constraints. Experimental results show that the proposed method consistently outperforms the baseline on multiple cross-domain detection tasks, demonstrating its effectiveness and good generalization ability under the source-free setting.