Detect object with DETR in a new domain unsupervised by randomly mask features with MTM
Detect object with DETR in a new domain unsupervised by randomly mask features with MTM
Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework
arXiv paper abstract https://arxiv.org/abs/2310.15646
arXiv PDF paper https://arxiv.org/pdf/2310.15646.pdf
Unsupervised domain adaptation object detection(UDAOD) research on Detection Transformer(DETR) mainly focuses on feature alignment and existing methods
... propose a two-stage framework named MTM ... Mean Teacher-DETR with Masked Feature Alignment.
In the pretraining stage, ... utilize labeled target-like images produced by image style transfer to avoid performance fluctuation.
In the self-training stage, ... leverage unlabeled target images by pseudo labels based on mean teacher and propose a module called Object Queries Knowledge Transfer(OQKT) to ensure consistent performance gains of the student model.
... propose masked feature alignment methods including Masked Domain Query-based Feature Alignment(MDQFA) and Masked Token-wise Feature Alignment(MTWFA) to alleviate domain shift ... prevent training stagnation and lead to a robust pretrained model in the pretraining stage ... enhance the model's target performance in the self-training stage.
... verify the effectiveness of MTM.
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