T
Thomas M. Breuel
Researcher at Nvidia
Publications - 240
Citations - 10811
Thomas M. Breuel is an academic researcher from Nvidia. The author has contributed to research in topics: Optical character recognition & Image segmentation. The author has an hindex of 43, co-authored 237 publications receiving 9547 citations. Previous affiliations of Thomas M. Breuel include Google & Xerox.
Papers
More filters
Proceedings Article
Unsupervised Image-to-Image Translation Networks
TL;DR: This work makes a shared-latent space assumption and proposes an unsupervised image-to-image translation framework based on Coupled GANs that achieves state-of-the-art performance on benchmark datasets.
Proceedings ArticleDOI
Large-scale visual sentiment ontology and detectors using adjective noun pairs
TL;DR: This work presents a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP) and proposes SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image.
Posted Content
Unsupervised Image-to-Image Translation Networks
TL;DR: In this paper, the authors make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs, which achieves state-of-the-art performance on benchmark datasets.
Journal ArticleDOI
Personalized search
James E. Pitkow,Hinrich Schutze,Todd A. Cass,Rob Cooley,Don Turnbull,Andrew N. Edmonds,Eytan Adar,Thomas M. Breuel +7 more
TL;DR: A contextual computing approach may prove a breakthrough in personalized search efficiency and lead to a new generation of search engines that combine natural language processing, artificial intelligence, and machine learning.
Proceedings ArticleDOI
Scene labeling with LSTM recurrent neural networks
TL;DR: The approach, which has a much lower computational complexity than prior methods, achieved state-of-the-art performance over the Stanford Background and the SIFT Flow datasets and the ability to visualize feature maps from each layer supports the hypothesis that LSTM networks are overall suited for image processing tasks.