Open-set domain adaptation is a develo** and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

For video models to be transferred and applied seamlessly across video tasks in varied
environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to …

Electroencephalography (EEG) provides reliable indications of human cognition and mental
states. Accurate emotion recognition from EEG remains challenging due to signal variations …

To enable video models to be applied seamlessly across video tasks in different
environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been …

Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train
models for unlabeled target data. Despite extensive research in domains like computer …

Abstract Video-based Unsupervised Domain Adaptation (UDA) methods concentrate on
addressing domain shift and improving the robustness of video models. It can be naturally …

Despite the progress seen in classification methods, current approaches for handling videos
with distribution shifts in source and target domains remain source-dependent as they …

This study provides a comprehensive benchmark framework for Source-Free Unsupervised
Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical …

Both few-shot learning and domain adaptation sub-fields in Computer Vision have seen
significant recent progress in terms of the availability of state-of-the-art algorithms and …

This article introduces a novel self-supervised method that leverages incoherence detection
for video representation learning. It stems from the observation that the visual system of …