Recognizing human activities from video sequences or still images is a challenging task due
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …

In the last decades, given the necessity of assisting fragile citizens, of which elderly
represent a significant portion, a considerable research effort has been devoted to the use of …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

Abstract Video-Language Pretraining (VLP), which aims to learn transferable representation
to advance a wide range of video-text downstream tasks, has recently received increasing …

We address the task of jointly determining what a person is doing and where they are
looking based on the analysis of video captured by a headworn camera. We propose a …

Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest
egocentric video benchmark, offering a unique viewpoint on people's interaction with …

We present an approach to capture the 3D structure and motion of a group of people
engaged in a social interaction. The core challenges in capturing social interactions are:(1) …

Activity detection and classification are very important for autonomous monitoring of humans
for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors …

Hands appear very often in egocentric video, and their appearance and pose give important
cues about what people are doing and what they are paying attention to. But existing work in …

Interpersonal relation defines the association, eg, warm, friendliness, and dominance,
between two or more people. We investigate if such fine-grained and high-level relation …