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7 papers · 2 filters
Asynchronous Temporal Fields for Action Recognition
Gunnar A. Sigurdsson, Santosh Divvala, Ali Farhadi +1
Actions are more than just movements and trajectories: we cook to eat and we hold a cup to drink from it. A thorough understanding of videos requires going beyond appearance modeli…
ActionFlowNet: Learning Motion Representation for Action Recognition
Joe Yue-Hei Ng, Jonghyun Choi, Jan Neumann +1
Even with the recent advances in convolutional neural networks (CNN) in various visual recognition tasks, the state-of-the-art action recognition system still relies on hand crafte…
LCNN: Lookup-based Convolutional Neural Network
Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhadi
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accur…
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang +3
Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer visi…
A Diagram Is Worth A Dozen Images
Aniruddha Kembhavi, Mike Salvato, Eric Kolve +3
Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Underst…
"What happens if..." Learning to Predict the Effect of Forces in Images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta +1
What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the…