256 citations · 334 across the 5 of their papers we have counts for
5 papers
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Michele Covell, Nick Johnston, David Minnen +5
We introduce a stop-code tolerant (SCT) approach to training recurrent convolutional neural networks for lossy image compression. Our methods introduce a multi-pass training method…
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Nick Johnston, Damien Vincent, David Minnen +6
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We…
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
Greg Mori, Caroline Pantofaru, Nisarg Kothari +4
We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human…
Efficient Large Scale Video Classification
Balakrishnan Varadarajan, George Toderici, Sudheendra Vijayanarasimhan +1
Video classification has advanced tremendously over the recent years. A large part of the improvements in video classification had to do with the work done by the image classificat…
Beyond Short Snippets: Deep Networks for Video Classification
Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan +3
Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrie…