Action recognition is a major branch of computer vision research. As a widely used
technology, action recognition has been applied to human–computer interaction, intelligent …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

The foundation models have recently shown excellent performance on a variety of
downstream tasks in computer vision. However, most existing vision foundation models …

Image-based visual-language (I-VL) pre-training has shown great success for learning joint
visual-textual representations from large-scale web data, revealing remarkable ability for …

In a real-world scenario, human actions are typically out of the distribution from training data,
which requires a model to both recognize the known actions and reject the unknown …

We approach the problem of learning by watching humans in the wild. While traditional
approaches in Imitation and Reinforcement Learning are promising for learning in the real …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

Understanding broadcast videos is a challenging task in computer vision, as it requires
generic reasoning capabilities to appreciate the content offered by the video editing. In this …

Action recognition is an open and challenging problem in computer vision. While current
state-of-the-art models offer excellent recognition results, their computational expense limits …

Videos of animal behavior are used to quantify researcher-defined behaviors of interest to
study neural function, gene mutations, and pharmacological therapies. Behaviors of interest …