4.3k citations · 5.5k across the 16 of their papers we have counts for
10 papers · 1 filter
Tracking Emerges by Colorizing Videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi +2
We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model…
The Devil is in the Decoder: Classification, Regression and GANs
Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama +4
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consi…
PixColor: Pixel Recursive Colorization
Sergio Guadarrama, Ryan Dahl, David Bieber +3
We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorizati…
Semantic Instance Segmentation via Deep Metric Learning
Alireza Fathi, Zbigniew Wojna, Vivek Rathod +4
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.…
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Siqi Liu, Zhenhai Zhu, Ning Ye +2
Current image captioning methods are usually trained via (penalized) maximum likelihood estimation. However, the log-likelihood score of a caption does not correlate well with huma…
Speed/accuracy trade-offs for modern convolutional object detectors
Jonathan Huang, Vivek Rathod, Chen Sun +8
The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To…
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach +4
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effe…
Compute Less to Get More: Using ORC to Improve Sparse Filtering
Johannes Lederer, Sergio Guadarrama
Sparse Filtering is a popular feature learning algorithm for image classification pipelines. In this paper, we connect the performance of Sparse Filtering with spectral properties…
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue +5
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The…
LSDA: Large Scale Detection Through Adaptation
Judy Hoffman, Sergio Guadarrama, Eric Tzeng +5
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) hav…