WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object Detection
CoRR(2023)
摘要
In the field of domain adaptation (DA) on 3D object detection, most of the
work is dedicated to unsupervised domain adaptation (UDA). Yet, without any
target annotations, the performance gap between the UDA approaches and the
fully-supervised approach is still noticeable, which is impractical for
real-world applications. On the other hand, weakly-supervised domain adaptation
(WDA) is an underexplored yet practical task that only requires few labeling
effort on the target domain. To improve the DA performance in a cost-effective
way, we propose a general weak labels guided self-training framework, WLST,
designed for WDA on 3D object detection. By incorporating autolabeler, which
can generate 3D pseudo labels from 2D bounding boxes, into the existing
self-training pipeline, our method is able to generate more robust and
consistent pseudo labels that would benefit the training process on the target
domain. Extensive experiments demonstrate the effectiveness, robustness, and
detector-agnosticism of our WLST framework. Notably, it outperforms previous
state-of-the-art methods on all evaluation tasks.
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