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68 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
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

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

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 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 18, 2018

Learning Patch Reconstructability for Accelerating Multi-View Stereo

Computer Vision and Pattern Recognition (CVPR)

We present an approach to accelerate multi-view stereo (MVS) by prioritizing computation on image patches that are likely to produce accurate 3D surface reconstructions. Our key insight is that the accuracy of the surface reconstruction from a given image patch can be predicted significantly faster than performing the actual stereo matching.

By: Alex Poms, Chenglei Wu, Shoou-I Yu, Yaser Sheikh
June 18, 2018

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Computer Vision and Pattern Recognition (CVPR)

Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.

By: Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
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

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
June 18, 2018

A Holistic Framework for Addressing the World using Machine Learning

Computer Vision and Pattern Recognition (CVPR)

Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery.

By: Ilke Demir, Forest Hughes, Aman Raj, Kaunil Dhruv, Suryanarayana Murthy Muddala, Sanyam Garg, Barrett Doo