As humans, we learn a lot about how to interact with the world by observing others
interacting with their hands. To help AI systems obtain a better understanding of hand …

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 …

Accurately estimating 3D hand pose is crucial for understanding how humans interact with
the world. Despite remarkable progress, existing methods often struggle to generate …

The ability to actively ground task instructions from an egocentric view is crucial for AI agents
to accomplish tasks or assist humans virtually. One important step towards this goal is to …

We aim to improve the performance of regressing hand keypoints and segmenting pixel-
level hand masks under new imaging conditions (eg., outdoors) when we only have labeled …

Existing IoU-based loss functions have achieved promising performance for bounding box
regression in object detection. However, they cannot fully reflect the relation between the …

Delving into the realm of egocentric vision, the advancement of referring video object
segmentation (RVOS) stands as pivotal in understanding human activities. However …

In this paper, we tackle the problem of Egocentric Human-Object Interaction (EHOI)
detection in an industrial setting. To overcome the lack of public datasets in this context, we …

In this study, we investigate the effectiveness of synthetic data in enhancing egocentric hand-
object interaction detection. Via extensive experiments and comparative analyses on three …

We consider the problem of detecting Egocentric Human-Object Interactions (EHOIs) in
industrial contexts. Since collecting and labeling large amounts of real images is …