Enhancing Visual Domain Adaptation with Source Preparation

• URL: http://arxiv.org/abs/2306.10142v1
• Date: Fri, 16 Jun 2023 18:56:44 GMT
• Title: Enhancing Visual Domain Adaptation with Source Preparation
• Authors: Anirudha Ramesh, Anurag Ghosh, Christoph Mertz, Jeff Schneider
• Abstract summary: Domain Adaptation techniques fail to consider the characteristics of the source domain itself. We propose Source Preparation (SP), a method to mitigate source domain biases. We show that SP enhances UDA across a range of visual domains, with improvements up to 40.64% in mIoU over baseline.
• Score: 5.287588907230967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic Perception in diverse domains such as low-light scenarios, where new modalities like thermal imaging and specialized night-vision sensors are increasingly employed, remains a challenge. Largely, this is due to the limited availability of labeled data. Existing Domain Adaptation (DA) techniques, while promising to leverage labels from existing well-lit RGB images, fail to consider the characteristics of the source domain itself. We holistically account for this factor by proposing Source Preparation (SP), a method to mitigate source domain biases. Our Almost Unsupervised Domain Adaptation (AUDA) framework, a label-efficient semi-supervised approach for robotic scenarios -- employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and Supervised Alignment (SA) from limited labeled data. We introduce CityIntensified, a novel dataset comprising temporally aligned image pairs captured from a high-sensitivity camera and an intensifier camera for semantic segmentation and object detection in low-light settings. We demonstrate the effectiveness of our method in semantic segmentation, with experiments showing that SP enhances UDA across a range of visual domains, with improvements up to 40.64% in mIoU over baseline, while making target models more robust to real-world shifts within the target domain. We show that AUDA is a label-efficient framework for effective DA, significantly improving target domain performance with only tens of labeled samples from the target domain.

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