493 citations · 889 across the 3 of their papers we have counts for
5 papers
X3D: Expanding Architectures for Efficient Video Recognition
Christoph Feichtenhofer
This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, wid…
What have we learned from deep representations for action recognition?
Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes +1
As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they a…
Detect to Track and Track to Detect
Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this pape…
Spatiotemporal Residual Networks for Video Action Recognition
Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new techni…
Convolutional Two-Stream Network Fusion for Video Action Recognition
Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion…