Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection

• URL: http://arxiv.org/abs/2302.06039v2
• Date: Mon, 28 Aug 2023 07:19:48 GMT
• Title: Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection
• Authors: Nicolas Harvey Chapman, Feras Dayoub, Will Browne and Christopher Lehnert
• Abstract summary: Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming changes in the general appearance of images. We propose a framework for explicitly addressing class distribution shift to improve pseudo-label reliability in self-training.
• Score: 2.5193191501662144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming changes in the general appearance of images. However, shifts in a robot's deployment environment can also impact the likelihood that different objects will occur, termed class distribution shift. Motivated by this, we propose a framework for explicitly addressing class distribution shift to improve pseudo-label reliability in self-training. Our approach uses the domain invariance and contextual understanding of a pre-trained joint vision and language model to predict the class distribution of unlabelled data. By aligning the class distribution of pseudo-labels with this prediction, we provide weak supervision of pseudo-label accuracy. To further account for low quality pseudo-labels early in self-training, we propose an approach to dynamically adjust the number of pseudo-labels per image based on model confidence. Our method outperforms state-of-the-art approaches on several benchmarks, including a 4.7 mAP improvement when facing challenging class distribution shift.

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