Research Area
Year Published

127 Results

September 9, 2018

Graph R-CNN for Scene Graph Generation

European Conference on Computer Vision (ECCV)

We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images.

By: Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh
September 8, 2018

DeepWrinkles: Accurate and Realistic Clothing Modeling

European Conference on Computer Vision (ECCV)

We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive computation of physics-based simulation (with numerous heuristic parameters), while models reconstructed from visual observations typically suffer from lack of geometric details.

By: Zorah Lähner, Daniel Cremers, Tony Tung
September 7, 2018

Joint Future Semantic and Instance Segmentation Prediction

ECCV Anticipating Human Behavior Workshop

In this work, we introduce a novel prediction approach that encodes instance and semantic segmentation information in a single representation based on distance maps.

By: Camille Couprie, Pauline Luc, Jakob Verbeek
September 7, 2018

Recycle-GAN: Unsupervised Video Retargeting

European Conference on Computer Vision (ECCV)

We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style.

By: Aayush Bansal, Shugao Ma, Deva Ramanan, Yaser Sheikh
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