Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …

Visual lifelogging consists of acquiring images that capture the daily experiences of the user
by wearing a camera over a long period of time. The pictures taken offer considerable …

Large pretrained (eg," foundation") models exhibit distinct capabilities depending on the
domain of data they are trained on. While these domains are generic, they may only barely …

In the past years, traditional pattern recognition methods have made great progress.
However, these methods rely heavily on manual feature extraction, which may hinder the …

We present a comprehensive framework for egocentric interaction recognition using
markerless 3D annotations of two hands manipulating objects. To this end, we propose a …

Humans navigate complex crowded environments based on social conventions: they
respect personal space, yielding right-of-way and avoid collisions. In our work, we propose a …

We present a unified framework for understanding 3D hand and object interactions in raw
image sequences from egocentric RGB cameras. Given a single RGB image, our model …

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 …

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

Understanding social interactions from egocentric views is crucial for many applications,
ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to …