From the 1 of 140 linked papers with an AI index.
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Unsupervised Learning of Spatiotemporally Coherent Metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson +2
Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coher…
Efficient Object Localization Using Convolutional Networks
Jonathan Tompson, Ross Goroshin, Arjun Jain +2
Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling a…
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
Arjun Jain, Jonathan Tompson, Yann LeCun +1
In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and…
Computing the Stereo Matching Cost with a Convolutional Neural Network
Jure Žbontar, Yann LeCun
We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to…
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Jonathan Tompson, Arjun Jain, Yann LeCun +1
This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the…
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Remi Denton, Wojciech Zaremba, Joan Bruna +2
We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy but…