3 accepted papers
This work tackles a key challenge in test-time energy adaptation: prohibitive time overhead arising from recent state-of-the-art test-time adaptation (TTA) methods, which are built on energy models relying on iterative Monte Carlo or Langevin dynamics sampling with multiple stochastic updates per te
Source-free unsupervised domain adaptation (SF-UDA), which relies only on a pre-trained source model and unlabeled target data, has gained significant attention. Pseudo-labeling, valued for its simplicity and effectiveness, is a key approach in SF-UDA. However, existing methods neglect the consisten
Video semantic segmentation aims to assign a class label for each pixel in every video frame. Existing methods predominantly follow the reference-target interaction paradigm, focusing on extracting local temporal contexts while neglecting the integration of global temporal information. Moreover, com…