R
Ross Girshick
Researcher at Facebook
Publications - 170
Citations - 336844
Ross Girshick is an academic researcher from Facebook. The author has contributed to research in topics: Object detection & Convolutional neural network. The author has an hindex of 97, co-authored 166 publications receiving 231744 citations. Previous affiliations of Ross Girshick include University of Washington & Carnegie Mellon University.
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Reducing Overfitting in Deep Networks by Decorrelating Representations
TL;DR: A new regularizer called DeCov is proposed which leads to significantly reduced overfitting, improved generalization performance, and better generalization in Deep Neural Networks.
Proceedings ArticleDOI
Training Deformable Part Models with Decorrelated Features
Ross Girshick,Jitendra Malik +1 more
TL;DR: This paper shows how to train a deformable part model (DPM) fast-typically in less than 20 minutes, or four times faster than the current fastest method-while maintaining high average precision on the PASCAL VOC datasets.
Posted Content
One-Bit Object Detection: On learning to localize objects with minimal supervision.
TL;DR: In this paper, a discriminative submodular cover problem is used to discover a set of positive object windows with a smoothed latent SVM formulation, which can leverage efficient Quasi-Newton optimization techniques.
Proceedings Article
Discriminatively Activated Sparselets
TL;DR: This paper describes a new training framework that learns which sparselets to activate in order to optimize a discriminative objective, leading to larger speedup factors with no decrease in task performance, and shows experimental results on object detection and image classification tasks.
Proceedings ArticleDOI
PyTorchVideo: A Deep Learning Library for Video Understanding
Haoqi Fan,Tullie Murrell,Heng Wang,Kalyan Vasudev Alwala,Yanghao Li,Yilei Li,Bo Xiong,Nikhila Ravi,Meng Li,Haichuan Yang,Jitendra Malik,Ross Girshick,Matt Feiszli,Aaron Adcock,Wan-Yen Lo,Christoph Feichtenhofer +15 more
TL;DR: PyTorchVideo as discussed by the authors is an open-source deep learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing.