3 accepted papers
Unsupervised domain adaptation (UDA) is an effective scheme to improve the performance of an object detector in foggy scenes by adapting labeled normal images (source domain) to unlabeled foggy images (target domain). Existing methods leverage the Teacher-Student mutual learning framework, <italic x
Fueled by the rapid evolution of robotics, the demand for intelligent and lightweight robotic systems continues to grow across industries. However, conventional designs often separate sensing and actuation, resulting in structural complexity and diminished reliability. While integrated sensor-actuat
It is challenging to annotate large-scale datasets for supervised video shadow detection methods. Using a model trained on labeled images to the video frames directly may lead to high generalization error and temporal inconsistent results. In this paper, we address these challenges by proposing a Sp…