Temporally localizing actions in a video is a fundamental challenge in video understanding.
Most existing approaches have often drawn inspiration from image object detection and …

Video classification is computationally expensive. In this paper, we address theproblem of
frame selection to reduce the computational cost of video classification. Recent work has …

We introduce a simple yet surprisingly powerful model to incorporate attention in action
recognition and human object interaction tasks. Our proposed attention module can be …

In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn
motion representations. Our representation flow layer is a fully-differentiable layer designed …

Recently, 3D Convolutional Neural Network (3D CNN) models have been widely studied for
video sequences and achieved satisfying performance in action recognition and detection …

Egocentric activity recognition is one of the most challenging tasks in video analysis. It
requires a fine-grained discrimination of small objects and their manipulation. While some …

Video-based action recognition with deep neural networks has shown remarkable progress.
However, most of the existing approaches are too computationally expensive due to the …

We present a new model to determine relative skill from long videos, through learnable
temporal attention modules. Skill determination is formulated as a ranking problem, making …

We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer
and present how it can be used to efficiently capture longer-term temporal information in …

In this paper, we introduce the concept of learning latent super-events from activity videos,
and present how it benefits activity detection in continuous videos. We define a super-event …