MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer

Understand

Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance.

• However, it requires large-scale labeled data and suffers from domain shift, especially when no labeled data is available in the target domain.
• To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.
• We further propose the comprehensive multi-level feature alignment to improve the pseudo labels generated by the mean teacher framework taking advantage of the cross-scale self-attention mechanism in Deformable DETR.