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0 | Graph-Structured Representations for Visual Question Answering | [
"Damien Teney",
"Lingqiao Liu",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Teney_Graph-Structured_Representations_for_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Teney_Graph-Structured_Representations_for_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Teney_Graph-Structured_Representations_for_2017_CVPR_supplemental.pdf | 1609.05600 | cvf | @InProceedings{Teney_2017_CVPR,author = {Teney, Damien and Liu, Lingqiao and van den Hengel, Anton},title = {Graph-Structured Representations for Visual Question Answering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reasoning over the visual and text domains. The predominant CNN/LSTM-based approach to VQA is limited by monolithic vector representations that ... | [
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1 | Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation | [
"Hao Zhao",
"Ming Lu",
"Anbang Yao",
"Yiwen Guo",
"Yurong Chen",
"Li Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhao_Physics_Inspired_Optimization_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Physics_Inspired_Optimization_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhao_Physics_Inspired_Optimization_2017_CVPR_supplemental.zip | 1707.00383 | cvf | @InProceedings{Zhao_2017_CVPR,author = {Zhao, Hao and Lu, Ming and Yao, Anbang and Guo, Yiwen and Chen, Yurong and Zhang, Li},title = {Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pat... | In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural... | [
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2 | Local Binary Convolutional Neural Networks | [
"Felix Juefei-Xu",
"Vishnu Naresh Boddeti",
"Marios Savvides"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Juefei-Xu_Local_Binary_Convolutional_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Juefei-Xu_Local_Binary_Convolutional_CVPR_2017_paper.pdf | null | 1608.06049 | cvf | @InProceedings{Juefei-Xu_2017_CVPR,author = {Juefei-Xu, Felix and Naresh Boddeti, Vishnu and Savvides, Marios},title = {Local Binary Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not u... | [
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3 | Designing Effective Inter-Pixel Information Flow for Natural Image Matting | [
"Yagiz Aksoy",
"Tunc Ozan Aydin",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Aksoy_Designing_Effective_Inter-Pixel_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Aksoy_Designing_Effective_Inter-Pixel_CVPR_2017_paper.pdf | null | 1707.05055 | cvf | @InProceedings{Aksoy_2017_CVPR,author = {Aksoy, Yagiz and Ozan Aydin, Tunc and Pollefeys, Marc},title = {Designing Effective Inter-Pixel Information Flow for Natural Image Matting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We present a novel, purely affinity-based natural image matting algorithm. Our method relies on carefully defined pixel-to-pixel connections that enable effective use of information available in the image and the trimap. We control the information flow from the known-opacity regions into the unknown region, as well as ... | [
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4 | Face Normals "In-The-Wild" Using Fully Convolutional Networks | [
"George Trigeorgis",
"Patrick Snape",
"Iasonas Kokkinos",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Trigeorgis_Face_Normals_In-The-Wild_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Trigeorgis_Face_Normals_In-The-Wild_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Trigeorgis_2017_CVPR,author = {Trigeorgis, George and Snape, Patrick and Kokkinos, Iasonas and Zafeiriou, Stefanos},title = {Face Normals "In-The-Wild" Using Fully Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},yea... | In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the tas... | [
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5 | 3D Face Morphable Models "In-The-Wild" | [
"James Booth",
"Epameinondas Antonakos",
"Stylianos Ploumpis",
"George Trigeorgis",
"Yannis Panagakis",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Booth_3D_Face_Morphable_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Booth_3D_Face_Morphable_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Booth_3D_Face_Morphable_2017_CVPR_supplemental.pdf | 1701.05360 | cvf | @InProceedings{Booth_2017_CVPR,author = {Booth, James and Antonakos, Epameinondas and Ploumpis, Stylianos and Trigeorgis, George and Panagakis, Yannis and Zafeiriou, Stefanos},title = {3D Face Morphable Models "In-The-Wild"},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVP... | 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. Howev... | [
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6 | Towards a Quality Metric for Dense Light Fields | [
"Vamsi Kiran Adhikarla",
"Marek Vinkler",
"Denis Sumin",
"Rafal K. Mantiuk",
"Karol Myszkowski",
"Hans-Peter Seidel",
"Piotr Didyk"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Adhikarla_Towards_a_Quality_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Adhikarla_Towards_a_Quality_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Adhikarla_Towards_a_Quality_2017_CVPR_supplemental.pdf | 1704.07576 | cvf | @InProceedings{Adhikarla_2017_CVPR,author = {Kiran Adhikarla, Vamsi and Vinkler, Marek and Sumin, Denis and Mantiuk, Rafal K. and Myszkowski, Karol and Seidel, Hans-Peter and Didyk, Piotr},title = {Towards a Quality Metric for Dense Light Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pa... | Light fields become a popular representation of three-dimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as w... | [
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7 | Position Tracking for Virtual Reality Using Commodity WiFi | [
"Manikanta Kotaru",
"Sachin Katti"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Kotaru_Position_Tracking_for_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Kotaru_Position_Tracking_for_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Kotaru_Position_Tracking_for_2017_CVPR_supplemental.zip | 1703.03468 | cvf | @InProceedings{Kotaru_2017_CVPR,author = {Kotaru, Manikanta and Katti, Sachin},title = {Position Tracking for Virtual Reality Using Commodity WiFi},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g., Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e.g., S... | [
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8 | Material Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion | [
"Kenichiro Tanaka",
"Yasuhiro Mukaigawa",
"Takuya Funatomi",
"Hiroyuki Kubo",
"Yasuyuki Matsushita",
"Yasushi Yagi"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Tanaka_Material_Classification_Using_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Tanaka_Material_Classification_Using_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Tanaka_Material_Classification_Using_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Tanaka_2017_CVPR,author = {Tanaka, Kenichiro and Mukaigawa, Yasuhiro and Funatomi, Takuya and Kubo, Hiroyuki and Matsushita, Yasuyuki and Yagi, Yasushi},title = {Material Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion},booktitle = {Proceedings of the IEEE Conference on Comp... | This paper presents a material classification method using an off-the-shelf Time-of-Flight (ToF) camera. We use a key observation that the depth measurement by a ToF camera is distorted in objects with certain materials, especially with translucent materials. We show that this distortion is caused by the variations of ... | [
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9 | Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks | [
"Philip Haeusser",
"Alexander Mordvintsev",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Haeusser_Learning_by_Association_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Haeusser_Learning_by_Association_CVPR_2017_paper.pdf | null | 1706.00909 | title_snapshot | @InProceedings{Haeusser_2017_CVPR,author = {Haeusser, Philip and Mordvintsev, Alexander and Cremers, Daniel},title = {Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by lear... | [
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10 | A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting | [
"Yvain Queau",
"Tao Wu",
"Francois Lauze",
"Jean-Denis Durou",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Queau_A_Non-Convex_Variational_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Queau_A_Non-Convex_Variational_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Queau_2017_CVPR,author = {Queau, Yvain and Wu, Tao and Lauze, Francois and Durou, Jean-Denis and Cremers, Daniel},title = {A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},... | This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-shadows, and enforces ... | [
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11 | Learning From Synthetic Humans | [
"Gul Varol",
"Javier Romero",
"Xavier Martin",
"Naureen Mahmood",
"Michael J. Black",
"Ivan Laptev",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Varol_Learning_From_Synthetic_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Varol_Learning_From_Synthetic_CVPR_2017_paper.pdf | null | 1701.01370 | cvf | @InProceedings{Varol_2017_CVPR,author = {Varol, Gul and Romero, Javier and Martin, Xavier and Mahmood, Naureen and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},title = {Learning From Synthetic Humans},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | Estimating human pose, shape, and motion from images and video are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to ext... | [
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12 | Correlational Gaussian Processes for Cross-Domain Visual Recognition | [
"Chengjiang Long",
"Gang Hua"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Long_Correlational_Gaussian_Processes_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Long_Correlational_Gaussian_Processes_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Long_2017_CVPR,author = {Long, Chengjiang and Hua, Gang},title = {Correlational Gaussian Processes for Cross-Domain Visual Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We present a probabilistic model that captures higher order co-occurrence statistics for joint visual recognition in a collection of images and across multiple domains. More importantly, we predict the structured output across multiple domains by correlating outputs from the multi-classes Gaussian process classifiers i... | [
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13 | Revisiting the Variable Projection Method for Separable Nonlinear Least Squares Problems | [
"Je Hyeong Hong",
"Christopher Zach",
"Andrew Fitzgibbon"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Hong_Revisiting_the_Variable_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Hong_Revisiting_the_Variable_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Hong_2017_CVPR,author = {Hyeong Hong, Je and Zach, Christopher and Fitzgibbon, Andrew},title = {Revisiting the Variable Projection Method for Separable Nonlinear Least Squares Problems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year ... | Variable Projection (VarPro) is a framework to solve optimization problems efficiently by optimally eliminating a subset of the unknowns. It is in particular adapted for Separable Nonlinear Least Squares (SNLS) problems, a class of optimization problems including low-rank matrix factorization with missing data and affi... | [
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14 | Learning to Detect Salient Objects With Image-Level Supervision | [
"Lijun Wang",
"Huchuan Lu",
"Yifan Wang",
"Mengyang Feng",
"Dong Wang",
"Baocai Yin",
"Xiang Ruan"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Learning_to_Detect_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Learning_to_Detect_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Wang_2017_CVPR,author = {Wang, Lijun and Lu, Huchuan and Wang, Yifan and Feng, Mengyang and Wang, Dong and Yin, Baocai and Ruan, Xiang},title = {Learning to Detect Salient Objects With Image-Level Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVP... | Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in salient object detection. However, training DNNs requires costly pixel-level annotations. In this paper, we leverage the observation that image-level tags provide important cues of foreground salient objects, and develop a weakly supervised... | [
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15 | Binary Coding for Partial Action Analysis With Limited Observation Ratios | [
"Jie Qin",
"Li Liu",
"Ling Shao",
"Bingbing Ni",
"Chen Chen",
"Fumin Shen",
"Yunhong Wang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Qin_Binary_Coding_for_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Qin_Binary_Coding_for_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Qin_2017_CVPR,author = {Qin, Jie and Liu, Li and Shao, Ling and Ni, Bingbing and Chen, Chen and Shen, Fumin and Wang, Yunhong},title = {Binary Coding for Partial Action Analysis With Limited Observation Ratios},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CV... | Traditional action recognition methods aim to recognize actions with complete observations/executions. However, it is often difficult to capture fully executed actions due to occlusions, interruptions, etc. Meanwhile, action prediction/recognition in advance based on partial observations is essential for preventing the... | [
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16 | Temporal Convolutional Networks for Action Segmentation and Detection | [
"Colin Lea",
"Michael D. Flynn",
"Rene Vidal",
"Austin Reiter",
"Gregory D. Hager"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Lea_Temporal_Convolutional_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Lea_Temporal_Convolutional_Networks_CVPR_2017_paper.pdf | null | 1611.05267 | cvf | @InProceedings{Lea_2017_CVPR,author = {Lea, Colin and Flynn, Michael D. and Vidal, Rene and Reiter, Austin and Hager, Gregory D.},title = {Temporal Convolutional Networks for Action Segmentation and Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that ca... | [
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17 | DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data | [
"Swaminathan Gurumurthy",
"Ravi Kiran Sarvadevabhatla",
"R. Venkatesh Babu"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Gurumurthy_DeLiGAN__Generative_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Gurumurthy_DeLiGAN__Generative_CVPR_2017_paper.pdf | null | 1706.02071 | cvf | @InProceedings{Gurumurthy_2017_CVPR,author = {Gurumurthy, Swaminathan and Kiran Sarvadevabhatla, Ravi and Venkatesh Babu, R.},title = {DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July}... | A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to cap... | [
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18 | Template Matching With Deformable Diversity Similarity | [
"Itamar Talmi",
"Roey Mechrez",
"Lihi Zelnik-Manor"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Talmi_Template_Matching_With_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Talmi_Template_Matching_With_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Talmi_Template_Matching_With_2017_CVPR_supplemental.pdf | 1612.02190 | cvf | @InProceedings{Talmi_2017_CVPR,author = {Talmi, Itamar and Mechrez, Roey and Zelnik-Manor, Lihi},title = {Template Matching With Deformable Diversity Similarity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a ... | [
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19 | Surface Motion Capture Transfer With Gaussian Process Regression | [
"Adnane Boukhayma",
"Jean-Sebastien Franco",
"Edmond Boyer"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Boukhayma_Surface_Motion_Capture_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Boukhayma_Surface_Motion_Capture_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Boukhayma_2017_CVPR,author = {Boukhayma, Adnane and Franco, Jean-Sebastien and Boyer, Edmond},title = {Surface Motion Capture Transfer With Gaussian Process Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We address the problem of transferring motion between captured 4D models. We particularly focus on human subjects for which the ability to automatically augment 4D datasets, by propagating movements between subjects, is of interest in a great deal of recent vision applications that builds on human visual corpus. Given ... | [
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20 | Generating Holistic 3D Scene Abstractions for Text-Based Image Retrieval | [
"Ang Li",
"Jin Sun",
"Joe Yue-Hei Ng",
"Ruichi Yu",
"Vlad I. Morariu",
"Larry S. Davis"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Li_Generating_Holistic_3D_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Generating_Holistic_3D_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Li_Generating_Holistic_3D_2017_CVPR_supplemental.pdf | 1611.09392 | cvf | @InProceedings{Li_2017_CVPR,author = {Li, Ang and Sun, Jin and Yue-Hei Ng, Joe and Yu, Ruichi and Morariu, Vlad I. and Davis, Larry S.},title = {Generating Holistic 3D Scene Abstractions for Text-Based Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mo... | Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer t... | [
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21 | Unsupervised Video Summarization With Adversarial LSTM Networks | [
"Behrooz Mahasseni",
"Michael Lam",
"Sinisa Todorovic"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Mahasseni_Unsupervised_Video_Summarization_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Mahasseni_Unsupervised_Video_Summarization_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Mahasseni_2017_CVPR,author = {Mahasseni, Behrooz and Lam, Michael and Todorovic, Sinisa},title = {Unsupervised Video Summarization With Adversarial LSTM Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames that optimally represent the input video. Our key idea is to learn a deep summarizer network to minimize distance between training videos and a distribution of their summarizations, in an unsupe... | [
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22 | SphereFace: Deep Hypersphere Embedding for Face Recognition | [
"Weiyang Liu",
"Yandong Wen",
"Zhiding Yu",
"Ming Li",
"Bhiksha Raj",
"Le Song"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Liu_SphereFace_Deep_Hypersphere_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_SphereFace_Deep_Hypersphere_CVPR_2017_paper.pdf | null | 1704.08063 | cvf | @InProceedings{Liu_2017_CVPR,author = {Liu, Weiyang and Wen, Yandong and Yu, Zhiding and Li, Ming and Raj, Bhiksha and Song, Le},title = {SphereFace: Deep Hypersphere Embedding for Face Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = ... | This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this en... | [
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23 | One-Shot Video Object Segmentation | [
"Sergi Caelles",
"Kevis-Kokitsi Maninis",
"Jordi Pont-Tuset",
"Laura Leal-Taixe",
"Daniel Cremers",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Caelles_One-Shot_Video_Object_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Caelles_One-Shot_Video_Object_CVPR_2017_paper.pdf | null | 1611.05198 | cvf | @InProceedings{Caelles_2017_CVPR,author = {Caelles, Sergi and Maninis, Kevis-Kokitsi and Pont-Tuset, Jordi and Leal-Taixe, Laura and Cremers, Daniel and Van Gool, Luc},title = {One-Shot Video Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively tr... | [
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24 | SGM-Nets: Semi-Global Matching With Neural Networks | [
"Akihito Seki",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Seki_SGM-Nets_Semi-Global_Matching_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Seki_SGM-Nets_Semi-Global_Matching_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Seki_SGM-Nets_Semi-Global_Matching_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Seki_2017_CVPR,author = {Seki, Akihito and Pollefeys, Marc},title = {SGM-Nets: Semi-Global Matching With Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper deals with deep neural networks for predicting accurate dense disparity map with Semi-global matching (SGM). SGM is a widely used regularization method for real scenes because of its high accuracy and fast computation speed. Even though SGM can obtain accurate results, tuning of SGM's penalty-parameters, whi... | [
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25 | What's in a Question: Using Visual Questions as a Form of Supervision | [
"Siddha Ganju",
"Olga Russakovsky",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Ganju_Whats_in_a_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Ganju_Whats_in_a_CVPR_2017_paper.pdf | null | 1704.03895 | cvf | @InProceedings{Ganju_2017_CVPR,author = {Ganju, Siddha and Russakovsky, Olga and Gupta, Abhinav},title = {What's in a Question: Using Visual Questions as a Form of Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Collecting fully annotated image datasets is challenging and expensive. Many types of weak supervision have been explored: weak manual annotations, web search results, temporal continuity, ambient sound and others. We focus on one particular unexplored mode: visual questions that are asked about images. The key observa... | [
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26 | Context-Aware Captions From Context-Agnostic Supervision | [
"Ramakrishna Vedantam",
"Samy Bengio",
"Kevin Murphy",
"Devi Parikh",
"Gal Chechik"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Vedantam_Context-Aware_Captions_From_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Vedantam_Context-Aware_Captions_From_CVPR_2017_paper.pdf | null | 1701.02870 | cvf | @InProceedings{Vedantam_2017_CVPR,author = {Vedantam, Ramakrishna and Bengio, Samy and Murphy, Kevin and Parikh, Devi and Chechik, Gal},title = {Context-Aware Captions From Context-Agnostic Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},yea... | We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of "siam... | [
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27 | Polyhedral Conic Classifiers for Visual Object Detection and Classification | [
"Hakan Cevikalp",
"Bill Triggs"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Cevikalp_Polyhedral_Conic_Classifiers_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Cevikalp_Polyhedral_Conic_Classifiers_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Cevikalp_2017_CVPR,author = {Cevikalp, Hakan and Triggs, Bill},title = {Polyhedral Conic Classifiers for Visual Object Detection and Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose a family of quasi-linear discriminants that outperform current large-margin methods in sliding window visual object detection and open set recognition tasks. In these tasks the classification problems are both numerically imbalanced -- positive (object class) training and test windows are much rarer than ... | [
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28 | Unsupervised Monocular Depth Estimation With Left-Right Consistency | [
"Clement Godard",
"Oisin Mac Aodha",
"Gabriel J. Brostow"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Godard_Unsupervised_Monocular_Depth_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Godard_Unsupervised_Monocular_Depth_CVPR_2017_paper.pdf | null | 1609.03677 | cvf | @InProceedings{Godard_2017_CVPR,author = {Godard, Clement and Mac Aodha, Oisin and Brostow, Gabriel J.},title = {Unsupervised Monocular Depth Estimation With Left-Right Consistency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality d... | [
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29 | Compact Matrix Factorization With Dependent Subspaces | [
"Viktor Larsson",
"Carl Olsson"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Larsson_Compact_Matrix_Factorization_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Larsson_Compact_Matrix_Factorization_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Larsson_Compact_Matrix_Factorization_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Larsson_2017_CVPR,author = {Larsson, Viktor and Olsson, Carl},title = {Compact Matrix Factorization With Dependent Subspaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. This imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of ... | [
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30 | Deep Reinforcement Learning-Based Image Captioning With Embedding Reward | [
"Zhou Ren",
"Xiaoyu Wang",
"Ning Zhang",
"Xutao Lv",
"Li-Jia Li"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Ren_Deep_Reinforcement_Learning-Based_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Ren_Deep_Reinforcement_Learning-Based_CVPR_2017_paper.pdf | null | 1704.03899 | cvf | @InProceedings{Ren_2017_CVPR,author = {Ren, Zhou and Wang, Xiaoyu and Zhang, Ning and Lv, Xutao and Li, Li-Jia},title = {Deep Reinforcement Learning-Based Image Captioning With Embedding Reward},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {201... | Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framewo... | [
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31 | Dual Attention Networks for Multimodal Reasoning and Matching | [
"Hyeonseob Nam",
"Jung-Woo Ha",
"Jeonghee Kim"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Nam_Dual_Attention_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Nam_Dual_Attention_Networks_CVPR_2017_paper.pdf | null | 1611.00471 | cvf | @InProceedings{Nam_2017_CVPR,author = {Nam, Hyeonseob and Ha, Jung-Woo and Kim, Jeonghee},title = {Dual Attention Networks for Multimodal Reasoning and Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend to specific regions in images and words in text through multiple steps and gather essential information from both modalities. Based on this ... | [
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32 | Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment | [
"Erik Wijmans",
"Yasutaka Furukawa"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wijmans_Exploiting_2D_Floorplan_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wijmans_Exploiting_2D_Floorplan_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Wijmans_Exploiting_2D_Floorplan_2017_CVPR_supplemental.pdf | 1612.02859 | cvf | @InProceedings{Wijmans_2017_CVPR,author = {Wijmans, Erik and Furukawa, Yasutaka},title = {Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We... | [
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33 | A Hierarchical Approach for Generating Descriptive Image Paragraphs | [
"Jonathan Krause",
"Justin Johnson",
"Ranjay Krishna",
"Li Fei-Fei"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Krause_A_Hierarchical_Approach_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Krause_A_Hierarchical_Approach_CVPR_2017_paper.pdf | null | 1611.06607 | cvf | @InProceedings{Krause_2017_CVPR,author = {Krause, Jonathan and Johnson, Justin and Krishna, Ranjay and Fei-Fei, Li},title = {A Hierarchical Approach for Generating Descriptive Image Paragraphs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017... | Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail. While one new captioning approach, dense captioning, can potentially describe images in finer lev... | [
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34 | Visual Dialog | [
"Abhishek Das",
"Satwik Kottur",
"Khushi Gupta",
"Avi Singh",
"Deshraj Yadav",
"Jose M. F. Moura",
"Devi Parikh",
"Dhruv Batra"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Das_Visual_Dialog_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Das_Visual_Dialog_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Das_Visual_Dialog_2017_CVPR_supplemental.pdf | 1611.08669 | cvf | @InProceedings{Das_2017_CVPR,author = {Das, Abhishek and Kottur, Satwik and Gupta, Khushi and Singh, Avi and Yadav, Deshraj and Moura, Jose M. F. and Parikh, Devi and Batra, Dhruv},title = {Visual Dialog},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},y... | We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, ... | [
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35 | DESIRE: Distant Future Prediction in Dynamic Scenes With Interacting Agents | [
"Namhoon Lee",
"Wongun Choi",
"Paul Vernaza",
"Christopher B. Choy",
"Philip H. S. Torr",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Lee_DESIRE_Distant_Future_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Lee_DESIRE_Distant_Future_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Lee_DESIRE_Distant_Future_2017_CVPR_supplemental.pdf | 1704.04394 | cvf | @InProceedings{Lee_2017_CVPR,author = {Lee, Namhoon and Choi, Wongun and Vernaza, Paul and Choy, Christopher B. and Torr, Philip H. S. and Chandraker, Manmohan},title = {DESIRE: Distant Future Prediction in Dynamic Scenes With Interacting Agents},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pa... | We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the ... | [
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36 | Mining Object Parts From CNNs via Active Question-Answering | [
"Quanshi Zhang",
"Ruiming Cao",
"Ying Nian Wu",
"Song-Chun Zhu"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Mining_Object_Parts_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Mining_Object_Parts_CVPR_2017_paper.pdf | null | 1704.03173 | cvf | @InProceedings{Zhang_2017_CVPR,author = {Zhang, Quanshi and Cao, Ruiming and Nian Wu, Ying and Zhu, Song-Chun},title = {Mining Object Parts From CNNs via Active Question-Answering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the ... | [
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37 | Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification | [
"Lifang He",
"Chun-Ta Lu",
"Hao Ding",
"Shen Wang",
"Linlin Shen",
"Philip S. Yu",
"Ann B. Ragin"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/He_Multi-Way_Multi-Level_Kernel_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/He_Multi-Way_Multi-Level_Kernel_CVPR_2017_paper.pdf | null | null | null | @InProceedings{He_2017_CVPR,author = {He, Lifang and Lu, Chun-Ta and Ding, Hao and Wang, Shen and Shen, Linlin and Yu, Philip S. and Ragin, Ann B.},title = {Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (... | Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation. Although many supervised tensor learning approaches have been proposed, they either cannot capture the nonlinear relationships of tensor data or cannot preserve ... | [
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38 | Low-Rank Bilinear Pooling for Fine-Grained Classification | [
"Shu Kong",
"Charless Fowlkes"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Kong_Low-Rank_Bilinear_Pooling_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Kong_Low-Rank_Bilinear_Pooling_CVPR_2017_paper.pdf | null | 1611.05109 | cvf | @InProceedings{Kong_2017_CVPR,author = {Kong, Shu and Fowlkes, Charless},title = {Low-Rank Bilinear Pooling for Fine-Grained Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Pooling second-order local feature statistics to form a high-dimensional bilinear feature has been shown to achieve state-of-the-art performance on a variety of fine-grained classification tasks. To address the computational demands of high feature dimensionality, we propose to represent the covariance features as a ma... | [
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39 | Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning | [
"Jiasen Lu",
"Caiming Xiong",
"Devi Parikh",
"Richard Socher"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Lu_Knowing_When_to_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Lu_Knowing_When_to_CVPR_2017_paper.pdf | null | 1612.01887 | cvf | @InProceedings{Lu_2017_CVPR,author = {Lu, Jiasen and Xiong, Caiming and Parikh, Devi and Socher, Richard},title = {Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year =... | Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as "the" and "of". Other words ... | [
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40 | Learning Deep Context-Aware Features Over Body and Latent Parts for Person Re-Identification | [
"Dangwei Li",
"Xiaotang Chen",
"Zhang Zhang",
"Kaiqi Huang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Li_Learning_Deep_Context-Aware_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Learning_Deep_Context-Aware_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Li_Learning_Deep_Context-Aware_2017_CVPR_supplemental.pdf | 1710.06555 | cvf | @InProceedings{Li_2017_CVPR,author = {Li, Dangwei and Chen, Xiaotang and Zhang, Zhang and Huang, Kaiqi},title = {Learning Deep Context-Aware Features Over Body and Latent Parts for Person Re-Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July}... | Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc. How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design... | [
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41 | Turning an Urban Scene Video Into a Cinemagraph | [
"Hang Yan",
"Yebin Liu",
"Yasutaka Furukawa"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Yan_Turning_an_Urban_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Yan_Turning_an_Urban_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Yan_Turning_an_Urban_2017_CVPR_supplemental.pdf | 1612.01235 | cvf | @InProceedings{Yan_2017_CVPR,author = {Yan, Hang and Liu, Yebin and Furukawa, Yasutaka},title = {Turning an Urban Scene Video Into a Cinemagraph},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper proposes an algorithm that turns a regular video capturing urban scenes into a high-quality endless animation, known as a Cinemagraph. The creation of a Cinemagraph usually requires a static camera in a carefully configured scene. The task becomes challenging for a regular video with a moving camera and obje... | [
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42 | Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-Identification | [
"Weihua Chen",
"Xiaotang Chen",
"Jianguo Zhang",
"Kaiqi Huang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Chen_Beyond_Triplet_Loss_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Beyond_Triplet_Loss_CVPR_2017_paper.pdf | null | 1704.01719 | cvf | @InProceedings{Chen_2017_CVPR,author = {Chen, Weihua and Chen, Xiaotang and Zhang, Jianguo and Huang, Kaiqi},title = {Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {201... | Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on... | [
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43 | Surveillance Video Parsing With Single Frame Supervision | [
"Si Liu",
"Changhu Wang",
"Ruihe Qian",
"Han Yu",
"Renda Bao",
"Yao Sun"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Liu_Surveillance_Video_Parsing_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_Surveillance_Video_Parsing_CVPR_2017_paper.pdf | null | 1611.09587 | cvf | @InProceedings{Liu_2017_CVPR,author = {Liu, Si and Wang, Changhu and Qian, Ruihe and Yu, Han and Bao, Renda and Sun, Yao},title = {Surveillance Video Parsing With Single Frame Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Surveillance video parsing, which segments the video frames into several labels, i.e., face, pants, left-leg, has wide applications. However, annotating all frames pixel-wisely is tedious and inefficient. In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per vide... | [
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44 | Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes | [
"Armin Mustafa",
"Adrian Hilton"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Mustafa_Semantically_Coherent_Co-Segmentation_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Mustafa_Semantically_Coherent_Co-Segmentation_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Mustafa_Semantically_Coherent_Co-Segmentation_2017_CVPR_supplemental.zip | null | null | @InProceedings{Mustafa_2017_CVPR,author = {Mustafa, Armin and Hilton, Adrian},title = {Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | In this paper we propose a framework for spatially and temporally coherent semantic co-segmentation and reconstruction of complex dynamic scenes from multiple static or moving cameras. Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and te... | [
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45 | Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection | [
"Guillermo Garcia-Hernando",
"Tae-Kyun Kim"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Garcia-Hernando_Transition_Forests_Learning_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Garcia-Hernando_Transition_Forests_Learning_CVPR_2017_paper.pdf | null | 1607.02737 | cvf | @InProceedings{Garcia-Hernando_2017_CVPR,author = {Garcia-Hernando, Guillermo and Kim, Tae-Kyun},title = {Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Jul... | A human action can be seen as transitions between one's body poses over time, where the transition depicts a temporal relation between two poses. Recognizing actions thus involves learning a classifier sensitive to these pose transitions as well as to static poses. In this paper, we introduce a novel method called tran... | [
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46 | Pixelwise Instance Segmentation With a Dynamically Instantiated Network | [
"Anurag Arnab",
"Philip H. S. Torr"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Arnab_Pixelwise_Instance_Segmentation_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Arnab_Pixelwise_Instance_Segmentation_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Arnab_Pixelwise_Instance_Segmentation_2017_CVPR_supplemental.pdf | 1704.02386 | cvf | @InProceedings{Arnab_2017_CVPR,author = {Arnab, Anurag and Torr, Philip H. S.},title = {Pixelwise Instance Segmentation With a Dynamically Instantiated Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level. We propose an Instance Segmentation system that produces a segmentation map where each pix... | [
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47 | Video Propagation Networks | [
"Varun Jampani",
"Raghudeep Gadde",
"Peter V. Gehler"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Jampani_Video_Propagation_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Jampani_Video_Propagation_Networks_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Jampani_Video_Propagation_Networks_2017_CVPR_supplemental.pdf | 1612.05478 | cvf | @InProceedings{Jampani_2017_CVPR,author = {Jampani, Varun and Gadde, Raghudeep and Gehler, Peter V.},title = {Video Propagation Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a "Video Propagation Network" that processes video frames in an... | [
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48 | Global Hypothesis Generation for 6D Object Pose Estimation | [
"Frank Michel",
"Alexander Kirillov",
"Eric Brachmann",
"Alexander Krull",
"Stefan Gumhold",
"Bogdan Savchynskyy",
"Carsten Rother"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Michel_Global_Hypothesis_Generation_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Michel_Global_Hypothesis_Generation_CVPR_2017_paper.pdf | null | 1612.02287 | cvf | @InProceedings{Michel_2017_CVPR,author = {Michel, Frank and Kirillov, Alexander and Brachmann, Eric and Krull, Alexander and Gumhold, Stefan and Savchynskyy, Bogdan and Rother, Carsten},title = {Global Hypothesis Generation for 6D Object Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Visio... | This paper addresses the task of estimating the 6D-pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) compute local features; ii) generate a pool of pose-hypotheses; iii) select and refine a pose from the pool. This work focuses on the second step. While all e... | [
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49 | Dilated Residual Networks | [
"Fisher Yu",
"Vladlen Koltun",
"Thomas Funkhouser"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Yu_Dilated_Residual_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Dilated_Residual_Networks_CVPR_2017_paper.pdf | null | 1705.09914 | cvf | @InProceedings{Yu_2017_CVPR,author = {Yu, Fisher and Koltun, Vladlen and Funkhouser, Thomas},title = {Dilated Residual Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downst... | [
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50 | Robust Interpolation of Correspondences for Large Displacement Optical Flow | [
"Yinlin Hu",
"Yunsong Li",
"Rui Song"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Hu_Robust_Interpolation_of_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Hu_Robust_Interpolation_of_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Hu_2017_CVPR,author = {Hu, Yinlin and Li, Yunsong and Song, Rui},title = {Robust Interpolation of Correspondences for Large Displacement Optical Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | The interpolation of correspondences (EpicFlow) was widely used for optical flow estimation in most-recent works. It has the advantage of edge-preserving and efficiency. However, it is vulnerable to input matching noise, which is inevitable in modern matching techniques. In this paper, we present a Robust Interpolation... | [
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51 | Supervising Neural Attention Models for Video Captioning by Human Gaze Data | [
"Youngjae Yu",
"Jongwook Choi",
"Yeonhwa Kim",
"Kyung Yoo",
"Sang-Hun Lee",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Yu_Supervising_Neural_Attention_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Supervising_Neural_Attention_CVPR_2017_paper.pdf | null | 1707.06029 | cvf | @InProceedings{Yu_2017_CVPR,author = {Yu, Youngjae and Choi, Jongwook and Kim, Yeonhwa and Yoo, Kyung and Lee, Sang-Hun and Kim, Gunhee},title = {Supervising Neural Attention Models for Video Captioning by Human Gaze Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)... | The attention mechanisms in deep neural networks are inspired by human's attention that sequentially focuses on the most relevant parts of the information over time to generate prediction output. The attention parameters in those models are implicitly trained in an end-to-end manner, yet there have been few trials to e... | [
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52 | Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks | [
"Hongsong Wang",
"Liang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Modeling_Temporal_Dynamics_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Modeling_Temporal_Dynamics_CVPR_2017_paper.pdf | null | 1704.02581 | cvf | @InProceedings{Wang_2017_CVPR,author = {Wang, Hongsong and Wang, Liang},title = {Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks ... | [
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53 | Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model | [
"Qixiang Ye",
"Tianliang Zhang",
"Wei Ke",
"Qiang Qiu",
"Jie Chen",
"Guillermo Sapiro",
"Baochang Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Ye_Self-Learning_Scene-Specific_Pedestrian_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Ye_Self-Learning_Scene-Specific_Pedestrian_CVPR_2017_paper.pdf | null | 1611.07544 | cvf | @InProceedings{Ye_2017_CVPR,author = {Ye, Qixiang and Zhang, Tianliang and Ke, Wei and Qiu, Qiang and Chen, Jie and Sapiro, Guillermo and Zhang, Baochang},title = {Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and P... | In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. The self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedure, object locations ... | [
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54 | Oriented Response Networks | [
"Yanzhao Zhou",
"Qixiang Ye",
"Qiang Qiu",
"Jianbin Jiao"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhou_Oriented_Response_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_Oriented_Response_Networks_CVPR_2017_paper.pdf | null | 1701.01833 | cvf | @InProceedings{Zhou_2017_CVPR,author = {Zhou, Yanzhao and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin},title = {Oriented Response Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global image rotations remains limited. In this paper, we propose Active Rotating Filters (ARFs) that actively rotate during convolution and produce ... | [
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55 | Video Acceleration Magnification | [
"Yichao Zhang",
"Silvia L. Pintea",
"Jan C. van Gemert"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhang_Video_Acceleration_Magnification_2017_CVPR_supplemental.zip | 1704.04186 | cvf | @InProceedings{Zhang_2017_CVPR,author = {Zhang, Yichao and Pintea, Silvia L. and van Gemert, Jan C.},title = {Video Acceleration Magnification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly. In this... | [
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56 | IRINA: Iris Recognition (Even) in Inaccurately Segmented Data | [
"Hugo Proenca",
"Joao C. Neves"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Proenca_IRINA_Iris_Recognition_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Proenca_IRINA_Iris_Recognition_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Proenca_2017_CVPR,author = {Proenca, Hugo and Neves, Joao C.},title = {IRINA: Iris Recognition (Even) in Inaccurately Segmented Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | The effectiveness of current iris recognition systems depends on the accurate segmentation and parameterisation of the iris boundaries, as failures at this point misalign the coefficients of the biometric signatures. This paper describes IRINA, an algorithm for Iris Recognition that is robust against INAccurately segme... | [
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57 | Forecasting Human Dynamics From Static Images | [
"Yu-Wei Chao",
"Jimei Yang",
"Brian Price",
"Scott Cohen",
"Jia Deng"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Chao_Forecasting_Human_Dynamics_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Chao_Forecasting_Human_Dynamics_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Chao_Forecasting_Human_Dynamics_2017_CVPR_supplemental.pdf | 1704.03432 | cvf | @InProceedings{Chao_2017_CVPR,author = {Chao, Yu-Wei and Yang, Jimei and Price, Brian and Cohen, Scott and Deng, Jia},title = {Forecasting Human Dynamics From Static Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on single-im... | [
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58 | Discriminative Bimodal Networks for Visual Localization and Detection With Natural Language Queries | [
"Yuting Zhang",
"Luyao Yuan",
"Yijie Guo",
"Zhiyuan He",
"I-An Huang",
"Honglak Lee"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Discriminative_Bimodal_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Discriminative_Bimodal_Networks_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhang_Discriminative_Bimodal_Networks_2017_CVPR_supplemental.pdf | 1704.03944 | cvf | @InProceedings{Zhang_2017_CVPR,author = {Zhang, Yuting and Yuan, Luyao and Guo, Yijie and He, Zhiyuan and Huang, I-An and Lee, Honglak},title = {Discriminative Bimodal Networks for Visual Localization and Detection With Natural Language Queries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pat... | Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accu... | [
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59 | A Linear Extrinsic Calibration of Kaleidoscopic Imaging System From Single 3D Point | [
"Kosuke Takahashi",
"Akihiro Miyata",
"Shohei Nobuhara",
"Takashi Matsuyama"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Takahashi_A_Linear_Extrinsic_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Takahashi_A_Linear_Extrinsic_CVPR_2017_paper.pdf | null | 1703.02826 | cvf | @InProceedings{Takahashi_2017_CVPR,author = {Takahashi, Kosuke and Miyata, Akihiro and Nobuhara, Shohei and Matsuyama, Takashi},title = {A Linear Extrinsic Calibration of Kaleidoscopic Imaging System From Single 3D Point},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | This paper proposes a new extrinsic calibration of kaleidoscopic imaging system by estimating normals and distances of the mirrors. The problem to be solved in this paper is a simultaneous estimation of all mirror parameters consistent throughout multiple reflections. Unlike conventional methods utilizing a pair of dir... | [
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60 | Efficient Multiple Instance Metric Learning Using Weakly Supervised Data | [
"Marc T. Law",
"Yaoliang Yu",
"Raquel Urtasun",
"Richard S. Zemel",
"Eric P. Xing"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Law_Efficient_Multiple_Instance_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Law_Efficient_Multiple_Instance_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Law_Efficient_Multiple_Instance_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Law_2017_CVPR,author = {Law, Marc T. and Yu, Yaoliang and Urtasun, Raquel and Zemel, Richard S. and Xing, Eric P.},title = {Efficient Multiple Instance Metric Learning Using Weakly Supervised Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | We consider learning a distance metric in a weakly supervised setting where "bags" (or sets) of instances are labeled with "bags" of labels. A general approach is to formulate the problem as a Multiple Instance Learning (MIL) problem where the metric is learned so that the distances between instances inferred to be sim... | [
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61 | Asynchronous Temporal Fields for Action Recognition | [
"Gunnar A. Sigurdsson",
"Santosh Divvala",
"Ali Farhadi",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Sigurdsson_Asynchronous_Temporal_Fields_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Sigurdsson_Asynchronous_Temporal_Fields_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Sigurdsson_Asynchronous_Temporal_Fields_2017_CVPR_supplemental.pdf | 1612.06371 | cvf | @InProceedings{Sigurdsson_2017_CVPR,author = {Sigurdsson, Gunnar A. and Divvala, Santosh and Farhadi, Ali and Gupta, Abhinav},title = {Asynchronous Temporal Fields for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Actions are more than just movements and trajectories: we cook to eat and we hold a cup to drink from it. A thorough understanding of videos requires going beyond appearance modeling and necessitates reasoning about the sequence of activities, as well as the higher-level constructs such as intentions. But how do we mod... | [
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62 | Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition With Convolutional Neural Networks | [
"Pichao Wang",
"Wanqing Li",
"Zhimin Gao",
"Yuyao Zhang",
"Chang Tang",
"Philip Ogunbona"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Scene_Flow_to_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Scene_Flow_to_CVPR_2017_paper.pdf | null | 1702.08652 | cvf | @InProceedings{Wang_2017_CVPR,author = {Wang, Pichao and Li, Wanqing and Gao, Zhimin and Zhang, Yuyao and Tang, Chang and Ogunbona, Philip},title = {Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition With Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Co... | Scene flow describes the motion of 3D objects in real world and potentially could be the basis of a good feature for 3D action recognition. However, its use for action recognition, especially in the context of convolutional neural networks (ConvNets), has not been previously studied. In this paper, we propose the extra... | [
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63 | A Point Set Generation Network for 3D Object Reconstruction From a Single Image | [
"Haoqiang Fan",
"Hao Su",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Fan_A_Point_Set_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Fan_A_Point_Set_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Fan_A_Point_Set_2017_CVPR_supplemental.pdf | 1612.00603 | cvf | @InProceedings{Fan_2017_CVPR,author = {Fan, Haoqiang and Su, Hao and Guibas, Leonidas J.},title = {A Point Set Generation Network for 3D Object Reconstruction From a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Generation of 3D data by deep neural network has been attracting increasing attention in the research community. The majority of extant works resort to regular representations such as volumetric grids or collection of images; however, these representations obscure the natural invariance of 3D shapes under geometric tra... | [
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64 | Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories | [
"Ziad Al-Halah",
"Rainer Stiefelhagen"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Al-Halah_Automatic_Discovery_Association_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Al-Halah_Automatic_Discovery_Association_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Al-Halah_Automatic_Discovery_Association_2017_CVPR_supplemental.pdf | 1704.03607 | title_snapshot | @InProceedings{Al-Halah_2017_CVPR,author = {Al-Halah, Ziad and Stiefelhagen, Rainer},title = {Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year ... | Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain exp... | [
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65 | Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution | [
"Wei-Sheng Lai",
"Jia-Bin Huang",
"Narendra Ahuja",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Lai_Deep_Laplacian_Pyramid_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Lai_Deep_Laplacian_Pyramid_CVPR_2017_paper.pdf | null | 1704.03915 | cvf | @InProceedings{Lai_2017_CVPR,author = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan},title = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {20... | Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes ... | [
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66 | Scene Parsing Through ADE20K Dataset | [
"Bolei Zhou",
"Hang Zhao",
"Xavier Puig",
"Sanja Fidler",
"Adela Barriuso",
"Antonio Torralba"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhou_Scene_Parsing_Through_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_Scene_Parsing_Through_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Zhou_2017_CVPR,author = {Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},title = {Scene Parsing Through ADE20K Dataset},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. I... | [
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67 | WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation | [
"Thibaut Durand",
"Taylor Mordan",
"Nicolas Thome",
"Matthieu Cord"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Durand_WILDCAT_Weakly_Supervised_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Durand_WILDCAT_Weakly_Supervised_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Durand_WILDCAT_Weakly_Supervised_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Durand_2017_CVPR,author = {Durand, Thibaut and Mordan, Taylor and Thome, Nicolas and Cord, Matthieu},title = {WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Patt... | This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features. Our model is trained using only global image labels and is devoted to three main visual recognition tasks: image classification, weakly supervised o... | [
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68 | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation | [
"Charles R. Qi",
"Hao Su",
"Kaichun Mo",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Qi_PointNet_Deep_Learning_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Qi_PointNet_Deep_Learning_2017_CVPR_supplemental.pdf | 1612.00593 | cvf | @InProceedings{Qi_2017_CVPR,author = {Qi, Charles R. and Su, Hao and Mo, Kaichun and Guibas, Leonidas J.},title = {PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}... | Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directl... | [
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69 | L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space | [
"Yurun Tian",
"Bin Fan",
"Fuchao Wu"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Tian_2017_CVPR,author = {Tian, Yurun and Fan, Bin and Wu, Fuchao},title = {L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | The research focus of designing local patch descriptors has gradually shifted from handcrafted ones (e.g., SIFT) to learned ones. In this paper, we propose to learn high per- formance descriptor in Euclidean space via the Convolu- tional Neural Network (CNN). Our method is distinctive in four aspects: (i) We propose a ... | [
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70 | Video Frame Interpolation via Adaptive Convolution | [
"Simon Niklaus",
"Long Mai",
"Feng Liu"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Niklaus_Video_Frame_Interpolation_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Niklaus_Video_Frame_Interpolation_CVPR_2017_paper.pdf | null | 1703.07514 | cvf | @InProceedings{Niklaus_2017_CVPR,author = {Niklaus, Simon and Mai, Long and Liu, Feng},title = {Video Frame Interpolation via Adaptive Convolution},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method conside... | [
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71 | Crossing Nets: Combining GANs and VAEs With a Shared Latent Space for Hand Pose Estimation | [
"Chengde Wan",
"Thomas Probst",
"Luc Van Gool",
"Angela Yao"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wan_Crossing_Nets_Combining_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wan_Crossing_Nets_Combining_CVPR_2017_paper.pdf | null | 1702.03431 | cvf | @InProceedings{Wan_2017_CVPR,author = {Wan, Chengde and Probst, Thomas and Van Gool, Luc and Yao, Angela},title = {Crossing Nets: Combining GANs and VAEs With a Shared Latent Space for Hand Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July}... | State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose modelling the statistical relationship of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for l... | [
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72 | Attention-Aware Face Hallucination via Deep Reinforcement Learning | [
"Qingxing Cao",
"Liang Lin",
"Yukai Shi",
"Xiaodan Liang",
"Guanbin Li"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Cao_Attention-Aware_Face_Hallucination_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Cao_Attention-Aware_Face_Hallucination_CVPR_2017_paper.pdf | null | 1708.03132 | cvf | @InProceedings{Cao_2017_CVPR,author = {Cao, Qingxing and Lin, Liang and Shi, Yukai and Liang, Xiaodan and Li, Guanbin},title = {Attention-Aware Face Hallucination via Deep Reinforcement Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {20... | Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between ... | [
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73 | Neural Scene De-Rendering | [
"Jiajun Wu",
"Joshua B. Tenenbaum",
"Pushmeet Kohli"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wu_Neural_Scene_De-Rendering_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wu_Neural_Scene_De-Rendering_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Wu_2017_CVPR,author = {Wu, Jiajun and Tenenbaum, Joshua B. and Kohli, Pushmeet},title = {Neural Scene De-Rendering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | e study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encodes information such as the number of objects and their categories, poses, positions, etc. Such a representation would allow us to reason about and even reconstruct or ... | [
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74 | Deep TEN: Texture Encoding Network | [
"Hang Zhang",
"Jia Xue",
"Kristin Dana"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Deep_TEN_Texture_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Deep_TEN_Texture_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhang_Deep_TEN_Texture_2017_CVPR_supplemental.pdf | 1612.02844 | cvf | @InProceedings{Zhang_2017_CVPR,author = {Zhang, Hang and Xue, Jia and Dana, Kristin},title = {Deep TEN: Texture Encoding Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose a Deep Texture Encoding Network (TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such ... | [
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75 | PolyNet: A Pursuit of Structural Diversity in Very Deep Networks | [
"Xingcheng Zhang",
"Zhizhong Li",
"Chen Change Loy",
"Dahua Lin"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_PolyNet_A_Pursuit_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_PolyNet_A_Pursuit_CVPR_2017_paper.pdf | null | 1611.05725 | cvf | @InProceedings{Zhang_2017_CVPR,author = {Zhang, Xingcheng and Li, Zhizhong and Change Loy, Chen and Lin, Dahua},title = {PolyNet: A Pursuit of Structural Diversity in Very Deep Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | A number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, however, we observed difficulties along both directions. On one hand, the pursuit for very deep networks is met with a diminishing return and... | [
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76 | Object Detection in Videos With Tubelet Proposal Networks | [
"Kai Kang",
"Hongsheng Li",
"Tong Xiao",
"Wanli Ouyang",
"Junjie Yan",
"Xihui Liu",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Kang_Object_Detection_in_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Kang_Object_Detection_in_CVPR_2017_paper.pdf | null | 1702.06355 | cvf | @InProceedings{Kang_2017_CVPR,author = {Kang, Kai and Li, Hongsheng and Xiao, Tong and Ouyang, Wanli and Yan, Junjie and Liu, Xihui and Wang, Xiaogang},title = {Object Detection in Videos With Tubelet Proposal Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},m... | Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos is vital for object detection. To fully utilize temporal information, state-of-the-art methods are based on s... | [
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77 | AMVH: Asymmetric Multi-Valued Hashing | [
"Cheng Da",
"Shibiao Xu",
"Kun Ding",
"Gaofeng Meng",
"Shiming Xiang",
"Chunhong Pan"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Da_AMVH_Asymmetric_Multi-Valued_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Da_AMVH_Asymmetric_Multi-Valued_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Da_2017_CVPR,author = {Da, Cheng and Xu, Shibiao and Ding, Kun and Meng, Gaofeng and Xiang, Shiming and Pan, Chunhong},title = {AMVH: Asymmetric Multi-Valued Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Most existing hashing methods resort to binary codes for similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose an asymmetric multi-valued hashing meth... | [
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78 | Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core | [
"Wadim Kehl",
"Federico Tombari",
"Slobodan Ilic",
"Nassir Navab"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Kehl_Real-Time_3D_Model_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Kehl_Real-Time_3D_Model_CVPR_2017_paper.pdf | null | 1911.10249 | cvf | @InProceedings{Kehl_2017_CVPR,author = {Kehl, Wadim and Tombari, Federico and Ilic, Slobodan and Navab, Nassir},title = {Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an order of magnitude while retaining similar accuracy. Furthermore, we show how the method can be made more robust in the presence of depth dat... | [
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79 | Weakly Supervised Action Learning With RNN Based Fine-To-Coarse Modeling | [
"Alexander Richard",
"Hilde Kuehne",
"Juergen Gall"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Richard_Weakly_Supervised_Action_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Richard_Weakly_Supervised_Action_CVPR_2017_paper.pdf | null | 1703.08132 | cvf | @InProceedings{Richard_2017_CVPR,author = {Richard, Alexander and Kuehne, Hilde and Gall, Juergen},title = {Weakly Supervised Action Learning With RNN Based Fine-To-Coarse Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We present an approach for weakly supervised learning of human actions. Given a set of videos and an ordered list of the occurring actions, the goal is to infer start and end frames of the related action classes within the video and to train the respective action classifiers without any need for hand labeled frame boun... | [
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80 | Differential Angular Imaging for Material Recognition | [
"Jia Xue",
"Hang Zhang",
"Kristin Dana",
"Ko Nishino"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Xue_Differential_Angular_Imaging_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Xue_Differential_Angular_Imaging_CVPR_2017_paper.pdf | null | 1612.02372 | cvf | @InProceedings{Xue_2017_CVPR,author = {Xue, Jia and Zhang, Hang and Dana, Kristin and Nishino, Ko},title = {Differential Angular Imaging for Material Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Material recognition for real-world outdoor surfaces has become increasingly important for computer vision to support its operation "in the wild." Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based... | [
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81 | Forecasting Interactive Dynamics of Pedestrians With Fictitious Play | [
"Wei-Chiu Ma",
"De-An Huang",
"Namhoon Lee",
"Kris M. Kitani"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Ma_Forecasting_Interactive_Dynamics_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Ma_Forecasting_Interactive_Dynamics_CVPR_2017_paper.pdf | null | 1604.01431 | cvf | @InProceedings{Ma_2017_CVPR,author = {Ma, Wei-Chiu and Huang, De-An and Lee, Namhoon and Kitani, Kris M.},title = {Forecasting Interactive Dynamics of Pedestrians With Fictitious Play},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory and deep learning-based visual analysis to estimate person-specific behavior parameters. We focus on predictive models since they are important for developing interactive autonomous syste... | [
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82 | Real-Time Neural Style Transfer for Videos | [
"Haozhi Huang",
"Hao Wang",
"Wenhan Luo",
"Lin Ma",
"Wenhao Jiang",
"Xiaolong Zhu",
"Zhifeng Li",
"Wei Liu"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Real-Time_Neural_Style_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Real-Time_Neural_Style_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Huang_Real-Time_Neural_Style_2017_CVPR_supplemental.zip | null | null | @InProceedings{Huang_2017_CVPR,author = {Huang, Haozhi and Wang, Hao and Luo, Wenhan and Ma, Lin and Jiang, Wenhao and Zhu, Xiaolong and Li, Zhifeng and Liu, Wei},title = {Real-Time Neural Style Transfer for Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain tem... | [
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83 | Incremental Kernel Null Space Discriminant Analysis for Novelty Detection | [
"Juncheng Liu",
"Zhouhui Lian",
"Yi Wang",
"Jianguo Xiao"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Liu_Incremental_Kernel_Null_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_Incremental_Kernel_Null_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Liu_Incremental_Kernel_Null_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Liu_2017_CVPR,author = {Liu, Juncheng and Lian, Zhouhui and Wang, Yi and Xiao, Jianguo},title = {Incremental Kernel Null Space Discriminant Analysis for Novelty Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Novelty detection, which aims to determine whether a given data belongs to any category of training data or not, is considered to be an important and challenging problem in areas of Pattern Recognition, Machine Learning, etc. Recently, kernel null space method (KNDA) was reported to have state-of-the-art performance in... | [
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84 | Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure From Motion | [
"Eisuke Ito",
"Takayuki Okatani"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Ito_Self-Calibration-Based_Approach_to_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Ito_Self-Calibration-Based_Approach_to_CVPR_2017_paper.pdf | null | 1611.05476 | cvf | @InProceedings{Ito_2017_CVPR,author = {Ito, Eisuke and Okatani, Takayuki},title = {Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure From Motion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM. Employing an RS camera model with linearized pure rotation, we show that the RS distortion can be approximately expressed by two internal parameters of an "imaginary" camera plus one-parameter nonlinear transformation similar to len... | [
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85 | Recurrent 3D Pose Sequence Machines | [
"Mude Lin",
"Liang Lin",
"Xiaodan Liang",
"Keze Wang",
"Hui Cheng"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Lin_Recurrent_3D_Pose_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Recurrent_3D_Pose_CVPR_2017_paper.pdf | null | 1707.09695 | cvf | @InProceedings{Lin_2017_CVPR,author = {Lin, Mude and Lin, Liang and Liang, Xiaodan and Wang, Keze and Cheng, Hui},title = {Recurrent 3D Pose Sequence Machines},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | 3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery. It is thus critical to exploit rich spatial and temporal long-range dependencies among body joints ... | [
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86 | Efficient Solvers for Minimal Problems by Syzygy-Based Reduction | [
"Viktor Larsson",
"Kalle Astrom",
"Magnus Oskarsson"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Larsson_Efficient_Solvers_for_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Larsson_Efficient_Solvers_for_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Larsson_2017_CVPR,author = {Larsson, Viktor and Astrom, Kalle and Oskarsson, Magnus},title = {Efficient Solvers for Minimal Problems by Syzygy-Based Reduction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | In this paper we study the problem of automatically generating polynomial solvers for minimal problems. The main contribution is a new method for finding small elimination templates by making use of the syzygies (i.e. the polynomial relations) that exist between the original equations. Using these syzygies we can essen... | [
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87 | Conditional Similarity Networks | [
"Andreas Veit",
"Serge Belongie",
"Theofanis Karaletsos"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Veit_Conditional_Similarity_Networks_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Veit_Conditional_Similarity_Networks_CVPR_2017_paper.pdf | null | 1603.07810 | cvf | @InProceedings{Veit_2017_CVPR,author = {Veit, Andreas and Belongie, Serge and Karaletsos, Theofanis},title = {Conditional Similarity Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one uniq... | [
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88 | Learning From Noisy Large-Scale Datasets With Minimal Supervision | [
"Andreas Veit",
"Neil Alldrin",
"Gal Chechik",
"Ivan Krasin",
"Abhinav Gupta",
"Serge Belongie"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Veit_Learning_From_Noisy_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Veit_Learning_From_Noisy_CVPR_2017_paper.pdf | null | 1701.01619 | cvf | @InProceedings{Veit_2017_CVPR,author = {Veit, Andreas and Alldrin, Neil and Chechik, Gal and Krasin, Ivan and Gupta, Abhinav and Belongie, Serge},title = {Learning From Noisy Large-Scale Datasets With Minimal Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune... | [
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89 | Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection | [
"Xiaodan Liang",
"Lisa Lee",
"Eric P. Xing"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Liang_Deep_Variation-Structured_Reinforcement_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Liang_Deep_Variation-Structured_Reinforcement_CVPR_2017_paper.pdf | null | 1703.03054 | cvf | @InProceedings{Liang_2017_CVPR,author = {Liang, Xiaodan and Lee, Lisa and Xing, Eric P.},title = {Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {20... | Despite progress in visual perception tasks such as image classification and detection, computers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among d... | [
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90 | Convolutional Random Walk Networks for Semantic Image Segmentation | [
"Gedas Bertasius",
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"Stella X. Yu",
"Jianbo Shi"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Bertasius_Convolutional_Random_Walk_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Bertasius_Convolutional_Random_Walk_CVPR_2017_paper.pdf | null | 1605.07681 | cvf | @InProceedings{Bertasius_2017_CVPR,author = {Bertasius, Gedas and Torresani, Lorenzo and Yu, Stella X. and Shi, Jianbo},title = {Convolutional Random Walk Networks for Semantic Image Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2... | Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resolution inside the deep layers. This leads to predictions with poor localization around the boundaries. Prior work has attempted to address th... | [
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91 | Predicting Ground-Level Scene Layout From Aerial Imagery | [
"Menghua Zhai",
"Zachary Bessinger",
"Scott Workman",
"Nathan Jacobs"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhai_Predicting_Ground-Level_Scene_2017_CVPR_supplemental.pdf | 1612.02709 | cvf | @InProceedings{Zhai_2017_CVPR,author = {Zhai, Menghua and Bessinger, Zachary and Workman, Scott and Jacobs, Nathan},title = {Predicting Ground-Level Scene Layout From Aerial Imagery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located ground imagery. Our network architecture takes an aerial image as input, ex... | [
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92 | Simple Does It: Weakly Supervised Instance and Semantic Segmentation | [
"Anna Khoreva",
"Rodrigo Benenson",
"Jan Hosang",
"Matthias Hein",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Khoreva_Simple_Does_It_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Khoreva_Simple_Does_It_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Khoreva_Simple_Does_It_2017_CVPR_supplemental.pdf | 1603.07485 | cvf | @InProceedings{Khoreva_2017_CVPR,author = {Khoreva, Anna and Benenson, Rodrigo and Hosang, Jan and Hein, Matthias and Schiele, Bernt},title = {Simple Does It: Weakly Supervised Instance and Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully desi... | [
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93 | Fast Fourier Color Constancy | [
"Jonathan T. Barron",
"Yun-Ta Tsai"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Barron_Fast_Fourier_Color_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Barron_Fast_Fourier_Color_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Barron_Fast_Fourier_Color_2017_CVPR_supplemental.pdf | 1611.07596 | cvf | @InProceedings{Barron_2017_CVPR,author = {Barron, Jonathan T. and Tsai, Yun-Ta},title = {Fast Fourier Color Constancy},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus. By operating in the frequency domain, FFCC produces lower error rates than the previous state-of-the-art by 13-20% while being 250-3000 times faster. T... | [
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94 | Attend to You: Personalized Image Captioning With Context Sequence Memory Networks | [
"Cesc Chunseong Park",
"Byeongchang Kim",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Park_Attend_to_You_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Park_Attend_to_You_CVPR_2017_paper.pdf | null | 1704.06485 | cvf | @InProceedings{Park_2017_CVPR,author = {Chunseong Park, Cesc and Kim, Byeongchang and Kim, Gunhee},title = {Attend to You: Personalized Image Captioning With Context Sequence Memory Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the user's active vocabularies in previous documents. As applications of personalized image captioning, we t... | [
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95 | Scalable Surface Reconstruction From Point Clouds With Extreme Scale and Density Diversity | [
"Christian Mostegel",
"Rudolf Prettenthaler",
"Friedrich Fraundorfer",
"Horst Bischof"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Mostegel_Scalable_Surface_Reconstruction_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Mostegel_Scalable_Surface_Reconstruction_CVPR_2017_paper.pdf | null | 1705.00949 | cvf | @InProceedings{Mostegel_2017_CVPR,author = {Mostegel, Christian and Prettenthaler, Rudolf and Fraundorfer, Friedrich and Bischof, Horst},title = {Scalable Surface Reconstruction From Point Clouds With Extreme Scale and Density Diversity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Rec... | In this paper we present a scalable approach for robustly computing a 3D surface mesh from multi-scale multi-view stereo point clouds that can handle extreme jumps of point density (in our experiments three orders of magnitude). The backbone of our approach is a combination of octree data partitioning, local Delaunay t... | [
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96 | Weakly Supervised Cascaded Convolutional Networks | [
"Ali Diba",
"Vivek Sharma",
"Ali Pazandeh",
"Hamed Pirsiavash",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Diba_Weakly_Supervised_Cascaded_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Diba_Weakly_Supervised_Cascaded_CVPR_2017_paper.pdf | null | 1611.08258 | cvf | @InProceedings{Diba_2017_CVPR,author = {Diba, Ali and Sharma, Vivek and Pazandeh, Ali and Pirsiavash, Hamed and Van Gool, Luc},title = {Weakly Supervised Cascaded Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Object detection is a challenging task in visual understanding domain, and even more so if the supervision is to be weak. Recently, few efforts to handle the task without expensive human annotations is established by promising deep neural network. A new architecture of cascaded networks is proposed to learn a convoluti... | [
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97 | Exclusivity-Consistency Regularized Multi-View Subspace Clustering | [
"Xiaobo Wang",
"Xiaojie Guo",
"Zhen Lei",
"Changqing Zhang",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Exclusivity-Consistency_Regularized_Multi-View_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Exclusivity-Consistency_Regularized_Multi-View_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Wang_2017_CVPR,author = {Wang, Xiaobo and Guo, Xiaojie and Lei, Zhen and Zhang, Changqing and Li, Stan Z.},title = {Exclusivity-Consistency Regularized Multi-View Subspace Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = ... | Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views... | [
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98 | Look Into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing | [
"Ke Gong",
"Xiaodan Liang",
"Dongyu Zhang",
"Xiaohui Shen",
"Liang Lin"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Gong_Look_Into_Person_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Gong_Look_Into_Person_CVPR_2017_paper.pdf | null | 1703.05446 | cvf | @InProceedings{Gong_2017_CVPR,author = {Gong, Ke and Liang, Xiaodan and Zhang, Dongyu and Shen, Xiaohui and Lin, Liang},title = {Look Into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitio... | Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a ne... | [
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... |
99 | Semi-Calibrated Near Field Photometric Stereo | [
"Fotios Logothetis",
"Roberto Mecca",
"Roberto Cipolla"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Logothetis_Semi-Calibrated_Near_Field_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Logothetis_Semi-Calibrated_Near_Field_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Logothetis_Semi-Calibrated_Near_Field_2017_CVPR_supplemental.zip | null | null | @InProceedings{Logothetis_2017_CVPR,author = {Logothetis, Fotios and Mecca, Roberto and Cipolla, Roberto},title = {Semi-Calibrated Near Field Photometric Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | 3D reconstruction from shading information through Photometric Stereo is considered a very challenging problem in Computer Vision. Although this technique can potentially provide highly detailed shape recovery, its accuracy is critically dependent on a numerous set of factors among them the reliability of the light sou... | [
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-0.02548297680914402,
-0.004199255257844925,
-0.040807195007801056,
... |
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