Search Results - "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"

Reinforcement Learning for Visual Object Detection by Mathe, Stefan, Pirinen, Aleksis, Sminchisescu, Cristian

“…One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have…”
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Image Style Transfer Using Convolutional Neural Networks by Gatys, Leon A., Ecker, Alexander S., Bethge, Matthias

“…Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches…”
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Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images by Shuran Song, Jianxiong Xiao

“…We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We…”
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Seven Ways to Improve Example-Based Single Image Super Resolution by Timofte, Radu, Rothe, Rasmus, Van Gool, Luc

“…In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2)…”
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Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video by Xiaowei Zhou, Menglong Zhu, Leonardos, Spyridon, Derpanis, Konstantinos G., Daniilidis, Kostas

“…This paper addresses the challenge of 3D full-body human pose estimation from a monocular image sequence. Here, two cases are considered: (i) the image…”
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Detection and Accurate Localization of Circular Fiducials under Highly Challenging Conditions by Calvet, Lilian, Gurdjos, Pierre, Griwodz, Carsten, Gasparini, Simone

“…Using fiducial markers ensures reliable detection and identification of planar features in images. Fiducials are used in a wide range of applications,…”
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Picking Deep Filter Responses for Fine-Grained Image Recognition by Xiaopeng Zhang, Hongkai Xiong, Wengang Zhou, Weiyao Lin, Qi Tian

“…Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific…”
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From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection by Jingjing Meng, Hongxing Wang, Junsong Yuan, Yap-Peng Tan

“…We propose to summarize a video into a few key objects by selecting representative object proposals generated from video frames. This representative selection…”
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Parametric Object Motion from Blur by Gast, Jochen, Sellent, Anita, Roth, Stefan

“…Motion blur can adversely affect a number of vision tasks, hence it is generally considered a nuisance. We instead treat motion blur as a useful signal that…”
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We are Humor Beings: Understanding and Predicting Visual Humor by Chandrasekaran, Arjun, Vijayakumar, Ashwin K., Antol, Stanislaw, Bansal, Mohit, Batra, Dhruv, Zitnick, C. Lawrence, Parikh, Devi

“…Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor…”
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Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC by Oskarsson, Magnus, Batstone, Kenneth, Astrom, Kalle

“…In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas, including…”
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Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity by Shah, Sohil, Goldstein, Tom, Studer, Christoph

“…Conventional algorithms for sparse signal recovery and sparse representation rely on l1-norm regularized variational methods. However, when applied to the…”
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Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification by Le Hou, Samaras, Dimitris, Kurc, Tahsin M., Yi Gao, Davis, James E., Saltz, Joel H.

“…Convolutional Neural Networks (CNN) are state-of-theart models for many image classification tasks. However, to recognize cancer subtypes automatically,…”
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Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

“…Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper…”
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Rethinking the Inception Architecture for Computer Vision by Szegedy, Christian, Vanhoucke, Vincent, Ioffe, Sergey, Shlens, Jon, Wojna, Zbigniew

“…Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional…”
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The Cityscapes Dataset for Semantic Urban Scene Understanding by Cordts, Marius, Omran, Mohamed, Ramos, Sebastian, Rehfeld, Timo, Enzweiler, Markus, Benenson, Rodrigo, Franke, Uwe, Roth, Stefan, Schiele, Bernt

“…Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from…”
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks by Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee

“…We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet…”
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network by Wenzhe Shi, Caballero, Jose, Huszar, Ferenc, Totz, Johannes, Aitken, Andrew P., Bishop, Rob, Rueckert, Daniel, Zehan Wang

“…Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for…”
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Structure-from-Motion Revisited by Schönberger, Johannes L., Frahm, Jan-Michael

“…Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have…”
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Context Encoders: Feature Learning by Inpainting by Pathak, Deepak, Krahenbuhl, Philipp, Donahue, Jeff, Darrell, Trevor, Efros, Alexei A.

“…We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context…”
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