1.8k citations · 3.6k across the 14 of their papers we have counts for
4 papers · 2 filters
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…
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
Li Wan, David Eigen, Rob Fergus
Deformable Parts Models and Convolutional Networks each have achieved notable performance in object detection. Yet these two approaches find their strengths in complementary areas:…
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
David Eigen, Rob Fergus
In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multi…
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
David Eigen, Christian Puhrsch, Rob Fergus
Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relation…