Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …

We present a retrospective on the state of Embodied AI research. Our analysis focuses on
13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are …

Humans naturally perceive surrounding scenes by unifying sound and sight in a first-person
view. Likewise, machines are advanced to approach human intelligence by learning with …

Self-supervised learning (SSL) has revolutionized visual representation learning, but has
not achieved the robustness of human vision. A reason for this could be that SSL does not …

Audio-visual zero-shot learning aims to classify samples consisting of a pair of
corresponding audio and video sequences from classes that are not present during training …

We propose a novel self-supervised embedding to learn how actions sound from narrated in-
the-wild egocentric videos. Whereas existing methods rely on curated data with known …

Abstract Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in
recent years for its critical role in creating emotion-aware intelligent machines. Previous …

We aim to learn to temporally localize object state changes and the corresponding state-
modifying actions by observing people interacting with objects in long uncurated web …

Human-object interaction (HOI) and temporal dynamics along the motion paths are the most
important visual cues for egocentric action anticipation. Especially, interaction regions …