• Publications
  • Influence
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of sceneExpand
Contour Detection and Hierarchical Image Segmentation
This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detectorExpand
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
TLDR
This work considers visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories and proposes a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. Expand
FractalNet: Ultra-Deep Neural Networks without Residuals
TLDR
In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Expand
Learning Representations for Automatic Colorization
TLDR
A fully automatic image colorization system that leverages recent advances in deep networks, exploiting both low-level and semantic representations, and explores colorization as a vehicle for self-supervised visual representation learning. Expand
From contours to regions: An empirical evaluation
TLDR
This work provides extensive experimental evaluation to demonstrate that, when coupled to a high-performance contour detector, the OWT-UCM algorithm produces state-of-the-art image segmentations. Expand
Using contours to detect and localize junctions in natural images
TLDR
A new high-performance contour detector using a combination of local and global cues that provides the best performance to date on the Berkeley Segmentation Dataset (BSDS) benchmark and shows that improvements in the contour model lead to better junctions. Expand
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression
TLDR
The strategy is to learn a convolutional neural network that directly predicts output albedo and shading channels from an input RGB image patch, which outperforms all prior work, including methods that rely on RGB+Depth input. Expand
From contours to regions: An empirical evaluation
TLDR
This work provides extensive experimental evaluation to demonstrate that, when coupled to a high-performance contour detector, the OWT-UCM algorithm produces state-of-the-art image segmentations. Expand
Occlusion boundary detection and figure/ground assignment from optical flow
In this work, we propose a contour and region detector for video data that exploits motion cues and distinguishes occlusion boundaries from internal boundaries based on optical flow. This detectorExpand
...
1
2
3
4
5
...