Debiased Teacher for Day-to-Night Domain Adaptive Object Detection

Abstract

Day-to-Night Domain Adaptive Object Detection (DN-DAOD) is a significant challenge due to the low visibility and signal-to-noise ratio at night. Although recent self-training approaches achieve promising results, they fail to address three critical biases: distribution bias, training bias, and confirmation bias. Therefore, we propose a Debiased Teacher to address the above biases from three aspects: domain transforming, representation compensating, and pseudo label calibrating. Concretely, the day-to-night domain transforming module (DNDT) leverages physical priors to model some key day-night domain differences, thus transforming daytime images into night-like images. Then, the cross-domain representation compensating module (CDRC) selectively mixes objects from nighttime and night-like images to compensate for the model's general representation of nighttime objects. Further, to correct confirmation bias caused by learning from inaccurate pseudo labels, the pseudo label confirmation calibrating module (ConCal) is designed to obtain accurate pseudo labels for better nighttime knowledge learning. Experimental results on three benchmarks demonstrate that our method outperforms current SOTA methods by a large margin.