Research Area
Year Published

143 Results

June 17, 2019

Graph-Based Global Reasoning Networks

Conference Computer Vision and Pattern Recognition (CVPR)

Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers. In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed.

By: Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis

June 16, 2019

Inverse Cooking: Recipe Generation from Food Images

Conference Computer Vision and Pattern Recognition (CVPR)

People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images.

By: Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero

June 16, 2019

Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition

Conference Computer Vision and Pattern Recognition (CVPR)

The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can only be reduced by acquiring additional measurements. In this paper, we present a novel method for MRI reconstruction that, at inference time, dynamically selects the measurements to take and iteratively refines the prediction in order to best reduce the reconstruction error and, thus, its uncertainty.

By: Zizhao Zhang, Adriana Romero, Matthew J. Muckley, Pascal Vincent, Lin Yang, Michal Drozdzal

June 16, 2019

Does Object Recognition Work for Everyone?

Conference Computer Vision and Pattern Recognition (CVPR)

The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition.

By: Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten

June 16, 2019

3D human pose estimation in video with temporal convolutions and semi-supervised training

Conference Computer Vision and Pattern Recognition (CVPR)

In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints.

By: Dario Pavllo, Christoph Feichtenhofer, David Grangier, Michael Auli

June 16, 2019

Towards VQA Models That Can Read

Conference Computer Vision and Pattern Recognition (CVPR)

Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem.

By: Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, Marcus Rohrbach

June 16, 2019

Leveraging the Present to Anticipate the Future in Videos

CVPR Precognition Workshop

Anticipating actions before they are executed is crucial for a wide range of practical applications including autonomous driving and robotics. While most prior work in this area requires partial observation of executed actions, in the paper we focus on anticipating actions seconds before they start. Our proposed approach is the fusion of a purely anticipatory model with a complementary model constrained to reason about the present.

By: Antoine Miech, Ivan Laptev, Josef Sivic, Heng Wang, Lorenzo Torresani, Du Tran

June 16, 2019

Kernel Transformer Networks for Compact Spherical Convolution

Conference Computer Vision and Pattern Recognition (CVPR)

Ideally, 360◦ imagery could inherit the deep convolutional neural networks (CNNs) already trained with great success on perspective projection images. However, existing methods to transfer CNNs from perspective to spherical images introduce significant computational costs and/or degradations in accuracy. We present the Kernel Transformer Network (KTN) to efficiently transfer convolution kernels from perspective images to the equirectangular projection of 360◦ images.

By: Yu-Chuan Su, Kristen Grauman

June 16, 2019

Building High Resolution Maps for Humanitarian Aid and Development with Weakly- and Semi-Supervised Learning

Computer Vision for Global Challenges Workshop at CVPR

Detailed maps help governments and NGOs plan infrastructure development and mobilize relief around the world. Mapping is an open-ended task with a seemingly endless number of potentially useful features to be mapped. In this work, we focus on mapping buildings and roads. We do so with techniques that could easily extend to other features such as land use and land classification. We discuss real-world use cases of our maps by NGOs and humanitarian organizations around the world—from sustainable infrastructure planning to disaster relief.

By: Derrick Bonafilia, David Yang, James Gill, Saikat Basu

June 15, 2019

Feature Denoising for Improving Adversarial Robustness

Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks.

By: Kaiming He, Yuxin Wu, Laurens van der Maaten, Alan Yuille, Cihang Xie