Recent studies have shown promising results on utilizing large pre-trained image-language
models for video question answering. While these image-language models can efficiently …

In this paper we tackle the problem of self-supervised video alignment and activity progress
prediction using in-the-wild videos. Our proposed self-supervised representation learning …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

Human motion analysis plays a crucial role in industry 4.0 and, more recently, in industry 5.0
where human-centered applications are becoming increasingly important, demonstrating its …

With the growth of high-quality data and advancement in visual pre-training paradigms,
Video Foundation Models (VFMs) have made significant progress recently, demonstrating …

Recently, self-supervised metric learning has raised attention for the potential to learn a
generic distance function. It overcomes the limitations of conventional supervised one, eg …

Unsupervised domain adaptation (UDA) in videos is a challenging task that remains not well
explored compared to image-based UDA techniques. Although vision transformers (ViT) …

Vision-language models (VLMs) have demonstrated remarkable performance across
various visual tasks, leveraging joint learning of visual and textual representations. While …