3.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nvidia (United Kingdom)GB136 papers
- Lawrence Berkeley National LaboratoryUS32 papers
- Stanford UniversityUS30 papers
- University of California, BerkeleyUS29 papers
- California Institute of TechnologyUS26 papers
- Massachusetts Institute of TechnologyUS25 papers
- Carnegie Mellon UniversityUS24 papers
- University of Illinois Urbana-ChampaignUS24 papers
- University of TorontoCA24 papers
- University of WashingtonUS23 papers
- Harvard UniversityUS19 papers
- Georgia Institute of TechnologyUS17 papers
6 papers · 2 filters
Learning Adaptive Parameter Tuning for Image Processing
Jingming Dong, Iuri Frosio, Jan Kautz
The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content. We propose a simple and flexible method to learn local tuning of…
Active Canny: Edge Detection and Recovery with Open Active Contour Models
Muhammet Bastan, S. Saqib Bukhari, Thomas M. Breuel
We introduce an edge detection and recovery framework based on open active contour models (snakelets). This is motivated by the noisy or broken edges output by standard edge detect…
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
Samuli Laine, Tero Karras, Timo Aila +5
We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins w…
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
Jinghua Wang, Zhenhua Wang, Dacheng Tao +2
In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We take advantage of deconvolutional networks which can predict pixel-wise class labels, and d…
Optical Flow with Semantic Segmentation and Localized Layers
Laura Sevilla-Lara, Deqing Sun, Varun Jampani +1
Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on…
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Song Han, Xingyu Liu, Huizi Mao +4
State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded…