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90 Results

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

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

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

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

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

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

CondenseNet: An Efficient DenseNet using Learned Group Convolutions

Computer Vision and Pattern Recognition (CVPR)

In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. 

By: Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger
June 18, 2018

Improving Landmark Localization with Semi-Supervised Learning

Computer Vision and Pattern Recognition (CVPR)

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available.

By: Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz
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