Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection

arXiv:2404.01988v2 Announce Type: replace
Abstract: Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles. To address this problem, we propose a \textbf{Co}operative \textbf{S}tudents (\textbf{CoS}) framework that innovatively employs global-local transformations (GLT) and a proxy-based target consistency (PTC) mechanism to capture the …

arxiv cs.cv detection domain domain adaptation object students type unsupervised