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550 Results

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 18, 2018

Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

Computer Vision and Pattern Recognition (CVPR)

We present a unified deformation model for the markerless capture of human movement at multiple scales, including facial expressions, body motion, and hand gestures.

By: Hanbyul Joo, Tomas Simon, Yaser Sheikh
June 18, 2018

Detecting and Recognizing Human-Object Interactions

Computer Vision and Pattern Recognition (CVPR)

In this paper, we address the task of detecting triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person – their pose, clothing, action – is a powerful cue for localizing the objects they are interacting with.

By: Georgia Gkioxari, Ross Girshick, Piotr Dollar, Kaiming He
June 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
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
June 18, 2018

DensePose: Dense Human Pose Estimation In The Wild

Computer Vision and Pattern Recognition (CVPR)

In this work we establish dense correspondences between an RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to train CNN-based systems that deliver dense correspondence ‘in the wild’, namely in the presence of background, occlusions and scale variations.

By: Riza Alp Guler, Natalia Neverova, Iasonas Kokkinos
June 18, 2018

DeepMVS: Learning Multi-view Stereopsis

Computer Vision and Pattern Recognition (CVPR)

We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction.

By: Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, Jia-Bin Huang
June 18, 2018

Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

Computer Vision and Pattern Recognition (CVPR)

In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow.

By: Xuanyi Dong, Shoou-I Yu, Xinshuo Weng, Shih-En Wei, Yi Yang, Yaser Sheikh
June 18, 2018

A Closer Look at Spatiotemporal Convolutions for Action Recognition

Computer Vision and Pattern Recognition (CVPR)

In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition.

By: Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, Manohar Paluri
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