94 Results
June 19, 2018
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Computer Vision and Pattern Recognition (CVPR)
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks (GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen.
By: Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, Ahmed Elgammal
Facebook AI Research June 18, 2018
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
CVPR Workshop - DeepGlobe 2018
Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018.
By: Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar
Computer Vision
Machine Learning June 18, 2018
Canonical Tensor Decomposition for Knowledge Base Completion
International Conference on Machine Learning (ICML)
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution. However, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their competitors. In this work, we attempt to understand the limits of CP for knowledge base completion.
By: Timothée Lacroix, Nicolas Usunier, Guillaume ObozinskiJune 18, 2018
Modeling Facial Geometry using Compositional VAEs
Computer Vision and Pattern Recognition (CVPR)
We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more local deformations.
By: Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh
Computer Vision
Machine Learning June 18, 2018
Eye In-Painting with Exemplar Generative Adversarial Networks
Computer Vision and Pattern Recognition (CVPR)
This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in-painting results.
By: Brian Dolhansky, Cristian Canton Ferrer
AR/VR June 18, 2018
A Two-Step Disentanglement Method
Computer Vision and Pattern Recognition (CVPR)
We address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not.
By: Naama Hadad, Lior Wolf, Moni Shahar
Computer Vision
Machine Learning June 18, 2018
Detect-and-Track: Efficient Pose Estimation in Videos
Computer Vision and Pattern Recognition (CVPR)
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection [17] and video understanding [5].
By: Rohit Girdhar, Georgia Gkioxari, Lorenzo Torresani, Manohar Paluri, Du TranJune 18, 2018
What Makes a Video a Video: Analyzing Temporal Information in Video Understanding Models and Datasets
Computer Vision and Pattern Recognition (CVPR)
While there have been numerous attempts at modeling motion in videos, an explicit analysis of the effect of temporal information for video understanding is still missing. In this work, we aim to bridge this gap and ask the following question: How important is the motion in the video for recognizing the action?
By: De-An Huang, Vignesh Ramanathan, Dhruv Mahajan, Lorenzo Torresani, Manohar Paluri, Li Fei-Fei, Juan Carlos Niebles
Computer Vision
Machine Learning June 18, 2018
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Computer Vision and Pattern Recognition (CVPR)
We introduce new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and use them to develop spatially-sparse convolutional networks.
By: Benjamin Graham, Laurens van der Maaten, Martin EngelckeJune 18, 2018
On the iterative refinement of densely connected representation levels for semantic segmentation
CVPR Workshop (CVPRW) on Autonomous Driving
In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN).
By: Arantxa Casanova, Guillem Cucurull, Michal Drozdzal, Adriana Romero, Yoshua Bengio