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Year Published

484 Results

June 18, 2018

Stacked Latent Attention for Multimodal Reasoning

Computer Vision and Pattern Recognition (CVPR)

Attention has shown to be a pivotal development in deep learning and has been used for a multitude of multimodal learning tasks such as visual question answering and image captioning. In this work, we pinpoint the potential limitations to the design of a traditional attention model.

By: Haoqi Fan, Jiatong Zhou
June 18, 2018

Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

Computer Vision and Pattern Recognition (CVPR)

In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian random fields.

By: Siddhartha Chandra, Camille Couprie, Iasonas Kokkinos
June 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
June 18, 2018

Separating Self-Expression and Visual Content in Hashtag Supervision

Computer Vision and Pattern Recognition (CVPR)

This paper presents an approach that extends upon modeling simple image-label pairs with a joint model of images, hashtags, and users. We demonstrate the efficacy of such approaches in image tagging and retrieval experiments, and show how the joint model can be used to perform user-conditional retrieval and tagging.

By: Andreas Veit, Maximilian Nickel, Serge Belongie, Laurens van der Maaten
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 Tran
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
June 18, 2018

Learning by Asking Questions

Computer Vision and Pattern Recognition (CVPR)

We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task.

By: Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten
June 18, 2018

Non-Local Neural Networks

Computer Vision and Pattern Recognition (CVPR)

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies.

By: Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming He
June 17, 2018

Unsupervised Correlation Analysis

Computer Vision and Pattern Recognition (CVPR)

Linking between two data sources is a basic building block in numerous computer vision problems. In this paper, we set to answer a fundamental cognitive question: are prior correspondences necessary for linking between different domains?

By: Yedid Hoshen, Lior Wolf
June 16, 2018

A common cause in the phenomenological and sensorimotor correlates of body ownership

International Multisensory Research Forum

The feeling that our limbs belong to our body is at the core of bodily self-consciousness. Over the years, limb ownership has been assessed through several types of measurements, including questionnaires and sensorimotor tasks assessing the perceived location of the hand with a visual-proprioceptive conflict.

By: Majed Samad, Cesare Parise, Sean Keller, Massimiliano Di Luca
Areas: AR/VR