Research Area
Year Published

144 Results

September 4, 2018

Mass Displacement Networks

British Machine Vision Convention (BMVC)

Despite the large improvements in performance attained by deep learning in computer vision, one can often further improve results with some additional post-processing that exploits the geometric nature of the underlying task. This commonly involves displacing the posterior distribution of a CNN in a way that makes it more appropriate for the task at hand, e.g. better aligned with local image features, or more compact. In this work we integrate this geometric post-processing within a deep architecture, introducing a differentiable and probabilistically sound counterpart to the common geometric voting technique used for evidence accumulation in vision.

By: Natalia Neverova, Iasonas Kokkinos
September 4, 2018

Self-Supervised Feature Learning for Semantic Segmentation of Overhead Imagery

British Machine Vision Convention (BMVC)

In this work, we study various self-supervised feature learning techniques for semantic segmentation of overhead imageries.

By: Suriya Singh, Anil Batra, Guan Pang, Lorenzo Torresani, Saikat Basu, Manohar Paluri, C.V. Jawahar
August 20, 2018

TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering

Knowledge Discovery in Databases (KDD)

In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms.

By: Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han
August 16, 2018

Constrained Bayesian Optimization with Noisy Experiments

Bayesian Analysis 2018

We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized.

By: Ben Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
August 14, 2018

Deep Appearance Models for Face Rendering


We introduce a deep appearance model for rendering the human face. Inspired by Active Appearance Models, we develop a data-driven rendering pipeline that learns a joint representation of facial geometry and appearance from a multiview capture setup.

By: Stephen Lombardi, Jason Saragih, Tomas Simon, Yaser Sheikh
August 13, 2018

Unsupervised Generation of Free-Form and Parameterized Avatars


We study two problems involving the task of mapping images between different domains. The first problem, transfers an image in one domain to an analog image in another domain. The second problem, extends the previous one by mapping an input image to a tied pair, consisting of a vector of parameters and an image that is created using a graphical engine from this vector of parameters.

By: Adam Polyak, Yaniv Taigman, Lior Wolf
July 29, 2018

Online Optical Marker-based Hand Tracking with Deep Labels

Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)

We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks.

By: Shangchen Han, Beibei Liu, Robert Wang, Yuting Ye, Christopher D. Twigg, Kenrick Kin
July 13, 2018

Analyzing Uncertainty in Neural Machine Translation

International Conference on Machine Learning (ICML)

Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data.

By: Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato
July 13, 2018

A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling

Association for Computational Linguistics (ACL)

We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling.

By: Ying Lin, Shengqi Yang, Veselin Stoyanov, Heng Ji
July 11, 2018

Convergent TREE BACKUP and RETRACE with Function Approximation

International Conference on Machine Learning (ICML)

In this work, we show that the TREE BACKUP and RETRACE algorithms are unstable with linear function approximation, both in theory and in practice with specific examples.

By: Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent