Search Results - "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition"

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) : Salt Lake City, Utah, USA 18-22 June 2018 by IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers

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CVPR 2019: Call for Papers Deadline November 16: The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition

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VizWiz Grand Challenge: Answering Visual Questions from Blind People by Gurari, Danna, Li, Qing, Stangl, Abigale J., Guo, Anhong, Lin, Chi, Grauman, Kristen, Luo, Jiebo, Bigham, Jeffrey P.

“…The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial…”
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Deep Ordinal Regression Network for Monocular Depth Estimation by Fu, Huan, Gong, Mingming, Wang, Chaohui, Batmanghelich, Kayhan, Tao, Dacheng

“…Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant…”
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InLoc: Indoor Visual Localization with Dense Matching and View Synthesis by Taira, Hajime, Okutomi, Masatoshi, Sattler, Torsten, Cimpoi, Mircea, Pollefeys, Marc, Sivic, Josef, Pajdla, Tomas, Torii, Akihiko

“…We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are…”
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Squeeze-and-Excitation Networks by Hu, Jie, Shen, Li, Sun, Gang

“…Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information…”
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MobileNetV2: Inverted Residuals and Linear Bottlenecks by Sandler, Mark, Howard, Andrew, Zhu, Menglong, Zhmoginov, Andrey, Chen, Liang-Chieh

“…In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and…”
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Non-local Neural Networks by Wang, Xiaolong, Girshick, Ross, Gupta, Abhinav, He, Kaiming

“…Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations…”
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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices by Zhang, Xiangyu, Zhou, Xinyu, Lin, Mengxiao, Sun, Jian

“…We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing…”
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Cascade R-CNN: Delving Into High Quality Object Detection by Cai, Zhaowei, Vasconcelos, Nuno

“…In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU…”
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs by Wang, Ting-Chun, Liu, Ming-Yu, Zhu, Jun-Yan, Tao, Andrew, Kautz, Jan, Catanzaro, Bryan

“…We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks…”
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Residual Dense Network for Image Super-Resolution by Zhang, Yulun, Tian, Yapeng, Kong, Yu, Zhong, Bineng, Fu, Yun

“…A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well…”
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Boosting Adversarial Attacks with Momentum by Dong, Yinpeng, Liao, Fangzhou, Pang, Tianyu, Su, Hang, Zhu, Jun, Hu, Xiaolin, Li, Jianguo

“…Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences…”
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High Performance Visual Tracking with Siamese Region Proposal Network by Li, Bo, Yan, Junjie, Wu, Wei, Zhu, Zheng, Hu, Xiaolin

“…Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on…”
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Frustum PointNets for 3D Object Detection from RGB-D Data by Qi, Charles R., Liu, Wei, Wu, Chenxia, Su, Hao, Guibas, Leonidas J.

“…In this work, we study 3D object detection from RGBD data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often…”
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Generative Image Inpainting with Contextual Attention by Yu, Jiahui, Lin, Zhe, Yang, Jimei, Shen, Xiaohui, Lu, Xin, Huang, Thomas S.

“…Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can…”
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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation by Saito, Kuniaki, Watanabe, Kohei, Ushiku, Yoshitaka, Harada, Tatsuya

“…In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the…”
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Deep Parametric Continuous Convolutional Neural Networks by Wang, Shenlong, Suo, Simon, Ma, Wei-Chiu, Pokrovsky, Andrei, Urtasun, Raquel

“…Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This…”
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Deep Mutual Learning by Zhang, Ying, Xiang, Tao, Hospedales, Timothy M., Lu, Huchuan

“…Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer…”
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3D Semantic Segmentation with Submanifold Sparse Convolutional Networks by Graham, Benjamin, Engelcke, Martin, Maaten, Laurens van der

“…Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is…”
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