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0 | Embodied Question Answering | [
"Abhishek Das",
"Samyak Datta",
"Georgia Gkioxari",
"Stefan Lee",
"Devi Parikh",
"Dhruv Batra"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Das_Embodied_Question_Answering_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Das_Embodied_Question_Answering_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0052-supp.pdf | arXiv:1711.11543 | cvf | @InProceedings{Das_2018_CVPR,author = {Das, Abhishek and Datta, Samyak and Gkioxari, Georgia and Lee, Stefan and Parikh, Devi and Batra, Dhruv},title = {Embodied Question Answering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather necessary visual information thro... | [
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1 | Learning by Asking Questions | [
"Ishan Misra",
"Ross Girshick",
"Rob Fergus",
"Martial Hebert",
"Abhinav Gupta",
"Laurens van der Maaten"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Misra_Learning_by_Asking_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Misra_Learning_by_Asking_CVPR_2018_paper.pdf | null | 1712.01238 | cvf | @InProceedings{Misra_2018_CVPR,author = {Misra, Ishan and Girshick, Ross and Fergus, Rob and Hebert, Martial and Gupta, Abhinav and van der Maaten, Laurens},title = {Learning by Asking Questions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {20... | We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and ... | [
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2 | Finding Tiny Faces in the Wild With Generative Adversarial Network | [
"Yancheng Bai",
"Yongqiang Zhang",
"Mingli Ding",
"Bernard Ghanem"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Bai_Finding_Tiny_Faces_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Bai_Finding_Tiny_Faces_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Bai_2018_CVPR,author = {Bai, Yancheng and Zhang, Yongqiang and Ding, Mingli and Ghanem, Bernard},title = {Finding Tiny Faces in the Wild With Generative Adversarial Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Face detection techniques have been developed for decades, and one of remaining open challenges is detecting small faces in unconstrained conditions. The reason is that tiny faces are often lacking detailed information and blurring. In this paper, we proposed an algorithm to directly generate a clear high-resolution fa... | [
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3 | Learning Face Age Progression: A Pyramid Architecture of GANs | [
"Hongyu Yang",
"Di Huang",
"Yunhong Wang",
"Anil K. Jain"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Learning_Face_Age_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Learning_Face_Age_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3633-supp.pdf | 1711.10352 | cvf | @InProceedings{Yang_2018_CVPR,author = {Yang, Hongyu and Huang, Di and Wang, Yunhong and Jain, Anil K.},title = {Learning Face Age Progression: A Pyramid Architecture of GANs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the a... | [
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4 | PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup | [
"Huiwen Chang",
"Jingwan Lu",
"Fisher Yu",
"Adam Finkelstein"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Chang_PairedCycleGAN_Asymmetric_Style_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Chang_PairedCycleGAN_Asymmetric_Style_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Chang_2018_CVPR,author = {Chang, Huiwen and Lu, Jingwan and Yu, Fisher and Finkelstein, Adam},title = {PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018... | This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial networks. Different from the image domain t... | [
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5 | GANerated Hands for Real-Time 3D Hand Tracking From Monocular RGB | [
"Franziska Mueller",
"Florian Bernard",
"Oleksandr Sotnychenko",
"Dushyant Mehta",
"Srinath Sridhar",
"Dan Casas",
"Christian Theobalt"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Mueller_GANerated_Hands_for_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Mueller_GANerated_Hands_for_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0736-supp.pdf | 1712.01057 | cvf | @InProceedings{Mueller_2018_CVPR,author = {Mueller, Franziska and Bernard, Florian and Sotnychenko, Oleksandr and Mehta, Dushyant and Sridhar, Srinath and Casas, Dan and Theobalt, Christian},title = {GANerated Hands for Real-Time 3D Hand Tracking From Monocular RGB},booktitle = {Proceedings of the IEEE Conference on Co... | We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a kinematic 3D hand model, such that it generalizes well to unseen data, is robust to occlusions and varying camera viewpoints, and leads to an... | [
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6 | Learning Pose Specific Representations by Predicting Different Views | [
"Georg Poier",
"David Schinagl",
"Horst Bischof"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Poier_Learning_Pose_Specific_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Poier_Learning_Pose_Specific_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1772-supp.pdf | 1804.03390 | cvf | @InProceedings{Poier_2018_CVPR,author = {Poier, Georg and Schinagl, David and Bischof, Horst},title = {Learning Pose Specific Representations by Predicting Different Views},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | The labeled data required to learn pose estimation for articulated objects is difficult to provide in the desired quantity, realism, density, and accuracy. To address this issue, we develop a method to learn representations, which are very specific for articulated poses, without the need for labeled training data. We e... | [
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7 | Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer | [
"Hao-Shu Fang",
"Guansong Lu",
"Xiaolin Fang",
"Jianwen Xie",
"Yu-Wing Tai",
"Cewu Lu"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Fang_Weakly_and_Semi_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Fang_Weakly_and_Semi_CVPR_2018_paper.pdf | null | 1805.04310 | cvf | @InProceedings{Fang_2018_CVPR,author = {Fang, Hao-Shu and Lu, Guansong and Fang, Xiaolin and Xie, Jianwen and Tai, Yu-Wing and Lu, Cewu},title = {Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit... | Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated imp... | [
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8 | Person Transfer GAN to Bridge Domain Gap for Person Re-Identification | [
"Longhui Wei",
"Shiliang Zhang",
"Wen Gao",
"Qi Tian"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Wei_Person_Transfer_GAN_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Person_Transfer_GAN_CVPR_2018_paper.pdf | null | 1711.08565 | cvf | @InProceedings{Wei_2018_CVPR,author = {Wei, Longhui and Zhang, Shiliang and Gao, Wen and Tian, Qi},title = {Person Transfer GAN to Bridge Domain Gap for Person Re-Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the ... | [
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9 | Cross-Modal Deep Variational Hand Pose Estimation | [
"Adrian Spurr",
"Jie Song",
"Seonwook Park",
"Otmar Hilliges"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Spurr_Cross-Modal_Deep_Variational_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Spurr_Cross-Modal_Deep_Variational_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3284-supp.pdf | 1803.11404 | cvf | @InProceedings{Spurr_2018_CVPR,author = {Spurr, Adrian and Song, Jie and Park, Seonwook and Hilliges, Otmar},title = {Cross-Modal Deep Variational Hand Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | The human hand moves in complex and high-dimensional ways, making estimation of 3D hand pose configurations from images alone a challenging task. In this work we propose a method to learn a statistical hand model represented by a cross-modal trained latent space via a generative deep neural network. We derive an object... | [
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10 | Disentangled Person Image Generation | [
"Liqian Ma",
"Qianru Sun",
"Stamatios Georgoulis",
"Luc Van Gool",
"Bernt Schiele",
"Mario Fritz"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Ma_Disentangled_Person_Image_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_Disentangled_Person_Image_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1801-supp.pdf | 1712.02621 | cvf | @InProceedings{Ma_2018_CVPR,author = {Ma, Liqian and Sun, Qianru and Georgoulis, Stamatios and Van Gool, Luc and Schiele, Bernt and Fritz, Mario},title = {Disentangled Person Image Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}... | Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangle... | [
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11 | Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs | [
"Adrian Bulat",
"Georgios Tzimiropoulos"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Bulat_Super-FAN_Integrated_Facial_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Bulat_Super-FAN_Integrated_Facial_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0568-supp.pdf | arXiv:1712.02765 | cvf | @InProceedings{Bulat_2018_CVPR,author = {Bulat, Adrian and Tzimiropoulos, Georgios},title = {Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition... | This paper addresses 2 challenging tasks: improving the quality of low resolution facial images and accurately locating the facial landmarks on such poor resolution images. To this end, we make the following 5 contributions: (a) we propose Super-FAN: the very first end-to-end system that addresses both tasks simultaneo... | [
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12 | Multistage Adversarial Losses for Pose-Based Human Image Synthesis | [
"Chenyang Si",
"Wei Wang",
"Liang Wang",
"Tieniu Tan"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Si_Multistage_Adversarial_Losses_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Si_Multistage_Adversarial_Losses_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Si_2018_CVPR,author = {Si, Chenyang and Wang, Wei and Wang, Liang and Tan, Tieniu},title = {Multistage Adversarial Losses for Pose-Based Human Image Synthesis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Human image synthesis has extensive practical applications e.g. person re-identification and data augmentation for human pose estimation. However, it is much more challenging than rigid object synthesis, e.g. cars and chairs, due to the variability of human posture. In this paper, we propose a pose-based human image sy... | [
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13 | Rotation Averaging and Strong Duality | [
"Anders Eriksson",
"Carl Olsson",
"Fredrik Kahl",
"Tat-Jun Chin"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Eriksson_Rotation_Averaging_and_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Eriksson_Rotation_Averaging_and_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0984-supp.pdf | 1705.01362 | cvf | @InProceedings{Eriksson_2018_CVPR,author = {Eriksson, Anders and Olsson, Carl and Kahl, Fredrik and Chin, Tat-Jun},title = {Rotation Averaging and Strong Duality},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this pro... | [
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14 | Hybrid Camera Pose Estimation | [
"Federico Camposeco",
"Andrea Cohen",
"Marc Pollefeys",
"Torsten Sattler"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Camposeco_Hybrid_Camera_Pose_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Camposeco_Hybrid_Camera_Pose_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2462-supp.pdf | null | null | @InProceedings{Camposeco_2018_CVPR,author = {Camposeco, Federico and Cohen, Andrea and Pollefeys, Marc and Sattler, Torsten},title = {Hybrid Camera Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In this paper, we aim to solve the pose estimation problem of calibrated pinhole and generalized cameras w.r.t. a Structure-from-Motion (SfM) model by leveraging both 2D-3D correspondences as well as 2D-2D correspondences. Traditional approaches either focus on the use of 2D-3D matches, known as structure-based pose es... | [
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15 | A Certifiably Globally Optimal Solution to the Non-Minimal Relative Pose Problem | [
"Jesus Briales",
"Laurent Kneip",
"Javier Gonzalez-Jimenez"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Briales_A_Certifiably_Globally_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Briales_A_Certifiably_Globally_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3968-supp.pdf | null | null | @InProceedings{Briales_2018_CVPR,author = {Briales, Jesus and Kneip, Laurent and Gonzalez-Jimenez, Javier},title = {A Certifiably Globally Optimal Solution to the Non-Minimal Relative Pose Problem},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | Finding the relative pose between two calibrated views ranks among the most fundamental geometric vision problems. It therefore appears as somewhat a surprise that a globally optimal solver that minimizes a properly defined energy over non-minimal correspondence sets and in the original space of relative transformation... | [
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16 | Single View Stereo Matching | [
"Yue Luo",
"Jimmy Ren",
"Mude Lin",
"Jiahao Pang",
"Wenxiu Sun",
"Hongsheng Li",
"Liang Lin"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Luo_Single_View_Stereo_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Single_View_Stereo_CVPR_2018_paper.pdf | null | 1803.02612 | cvf | @InProceedings{Luo_2018_CVPR,author = {Luo, Yue and Ren, Jimmy and Lin, Mude and Pang, Jiahao and Sun, Wenxiu and Li, Hongsheng and Lin, Liang},title = {Single View Stereo Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Previous monocular depth estimation methods take a single view and directly regress the expected results. Though recent advances are made by applying geometrically inspired loss functions during training, the inference procedure does not explicitly impose any geometrical constraint. Therefore these models purely rely o... | [
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17 | Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading | [
"Bjoern Haefner",
"Yvain Quéau",
"Thomas Möllenhoff",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Haefner_Fight_Ill-Posedness_With_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Haefner_Fight_Ill-Posedness_With_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2980-supp.pdf | null | null | @InProceedings{Haefner_2018_CVPR,author = {Haefner, Bjoern and Quéau, Yvain and Möllenhoff, Thomas and Cremers, Daniel},title = {Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition... | We put forward a principled variational approach for up-sampling a single depth map to the resolution of the companion color image provided by an RGB-D sensor. We combine heterogeneous depth and color data in order to jointly solve the ill-posed depth super-resolution and shape-from-shading problems. The low-frequency ... | [
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18 | Deep Depth Completion of a Single RGB-D Image | [
"Yinda Zhang",
"Thomas Funkhouser"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Deep_Depth_Completion_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Deep_Depth_Completion_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3324-supp.pdf | arXiv:1803.09326 | cvf | @InProceedings{Zhang_2018_CVPR,author = {Zhang, Yinda and Funkhouser, Thomas},title = {Deep Depth Completion of a Single RGB-D Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion bounda... | [
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19 | Multi-View Harmonized Bilinear Network for 3D Object Recognition | [
"Tan Yu",
"Jingjing Meng",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Yu_2018_CVPR,author = {Yu, Tan and Meng, Jingjing and Yuan, Junsong},title = {Multi-View Harmonized Bilinear Network for 3D Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | View-based methods have achieved considerable success in $3$D object recognition tasks. Different from existing view-based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilin... | [
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20 | PPFNet: Global Context Aware Local Features for Robust 3D Point Matching | [
"Haowen Deng",
"Tolga Birdal",
"Slobodan Ilic"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Deng_PPFNet_Global_Context_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_PPFNet_Global_Context_CVPR_2018_paper.pdf | null | 1802.02669 | cvf | @InProceedings{Deng_2018_CVPR,author = {Deng, Haowen and Birdal, Tolga and Ilic, Slobodan},title = {PPFNet: Global Context Aware Local Features for Robust 3D Point Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is ... | [
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21 | FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation | [
"Yaoqing Yang",
"Chen Feng",
"Yiru Shen",
"Dong Tian"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1129-supp.pdf | 1712.07262 | cvf | @InProceedings{Yang_2018_CVPR,author = {Yang, Yaoqing and Feng, Chen and Shen, Yiru and Tian, Dong},title = {FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. O... | [
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22 | A Papier-Mâché Approach to Learning 3D Surface Generation | [
"Thibault Groueix",
"Matthew Fisher",
"Vladimir G. Kim",
"Bryan C. Russell",
"Mathieu Aubry"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Groueix_A_Papier-Mache_Approach_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Groueix_A_Papier-Mache_Approach_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1775-supp.pdf | 1802.05384 | cvf | @InProceedings{Groueix_2018_CVPR,author = {Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan C. and Aubry, Mathieu},title = {A Papier-Mâché Approach to Learning 3D Surface Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {... | We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generat... | [
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23 | LEGO: Learning Edge With Geometry All at Once by Watching Videos | [
"Zhenheng Yang",
"Peng Wang",
"Yang Wang",
"Wei Xu",
"Ram Nevatia"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yang_LEGO_Learning_Edge_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_LEGO_Learning_Edge_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2629-supp.zip | 1803.05648 | cvf | @InProceedings{Yang_2018_CVPR,author = {Yang, Zhenheng and Wang, Peng and Wang, Yang and Xu, Wei and Nevatia, Ram},title = {LEGO: Learning Edge With Geometry All at Once by Watching Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a “3D as-smooth-as-possible (3D-ASAP)” prior inside the pipeline, which enables joint estimation of edges and 3D scene, yielding results with si... | [
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24 | Five-Point Fundamental Matrix Estimation for Uncalibrated Cameras | [
"Daniel Barath"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Barath_Five-Point_Fundamental_Matrix_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Five-Point_Fundamental_Matrix_CVPR_2018_paper.pdf | null | 1803.00260 | cvf | @InProceedings{Barath_2018_CVPR,author = {Barath, Daniel},title = {Five-Point Fundamental Matrix Estimation for Uncalibrated Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e.g. the SIFT detector. The proposed minimal solver first estimates a homography from three correspondences assuming that they are co-planar and exploiting their rotational components. Then the ... | [
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25 | PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation | [
"Danfei Xu",
"Dragomir Anguelov",
"Ashesh Jain"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Xu_PointFusion_Deep_Sensor_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_PointFusion_Deep_Sensor_CVPR_2018_paper.pdf | null | 1711.10871 | cvf | @InProceedings{Xu_2018_CVPR,author = {Xu, Danfei and Anguelov, Dragomir and Jain, Ashesh},title = {PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information. Unlike existing methods that either use multi-stage pipelines or hold sensor and dataset-specific assumptions, PointFusion is conceptually simple and application-agnostic. The image data and the raw po... | [
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26 | Scalable Dense Non-Rigid Structure-From-Motion: A Grassmannian Perspective | [
"Suryansh Kumar",
"Anoop Cherian",
"Yuchao Dai",
"Hongdong Li"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Kumar_Scalable_Dense_Non-Rigid_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Kumar_Scalable_Dense_Non-Rigid_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1350-supp.pdf | arXiv:1803.00233 | cvf | @InProceedings{Kumar_2018_CVPR,author = {Kumar, Suryansh and Cherian, Anoop and Dai, Yuchao and Li, Hongdong},title = {Scalable Dense Non-Rigid Structure-From-Motion: A Grassmannian Perspective},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {201... | This paper addresses the task of dense non-rigid structure-from-motion (NRSfM) using multiple images. State-of-the-art methods to this problem are often hurdled by scalability, expensive computations, and noisy measurements. Further, recent methods to NRSfM usually either assume a small number of sparse feature points ... | [
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27 | GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition | [
"Yifan Feng",
"Zizhao Zhang",
"Xibin Zhao",
"Rongrong Ji",
"Yue Gao"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Feng_GVCNN_Group-View_Convolutional_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Feng_GVCNN_Group-View_Convolutional_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Feng_2018_CVPR,author = {Feng, Yifan and Zhang, Zizhao and Zhao, Xibin and Ji, Rongrong and Gao, Yue},title = {GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | 3D shape recognition has attracted much attention recently. Its recent advances advocate the usage of deep features and achieve the state-of-the-art performance. However, existing deep features for 3D shape recognition are restricted to a view-to-shape setting, which learns the shape descriptor from the view-level feat... | [
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28 | Depth and Transient Imaging With Compressive SPAD Array Cameras | [
"Qilin Sun",
"Xiong Dun",
"Yifan Peng",
"Wolfgang Heidrich"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Sun_Depth_and_Transient_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Sun_Depth_and_Transient_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Sun_2018_CVPR,author = {Sun, Qilin and Dun, Xiong and Peng, Yifan and Heidrich, Wolfgang},title = {Depth and Transient Imaging With Compressive SPAD Array Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Time-of-flight depth imaging and transient imaging are two imaging modalities that have recently received a lot of interest. Despite much research, existing hardware systems are limited either in terms of temporal resolution or are prohibitively expensive. Arrays of Single Photon Avalanche Diodes (SPADs) promise to fil... | [
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29 | GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation | [
"Xiaojuan Qi",
"Renjie Liao",
"Zhengzhe Liu",
"Raquel Urtasun",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Qi_GeoNet_Geometric_Neural_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_GeoNet_Geometric_Neural_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Qi_2018_CVPR,author = {Qi, Xiaojuan and Liao, Renjie and Liu, Zhengzhe and Urtasun, Raquel and Jia, Jiaya},title = {GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal- to-depth networks. Depth-to-normal net... | [
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30 | Real-Time Seamless Single Shot 6D Object Pose Prediction | [
"Bugra Tekin",
"Sudipta N. Sinha",
"Pascal Fua"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Tekin_Real-Time_Seamless_Single_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Tekin_Real-Time_Seamless_Single_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3117-supp.pdf | 1711.08848 | cvf | @InProceedings{Tekin_2018_CVPR,author = {Tekin, Bugra and Sinha, Sudipta N. and Fua, Pascal},title = {Real-Time Seamless Single Shot 6D Object Pose Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [Kehl et al. 2017] that only predicts an approximate 6D pose that... | [
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31 | Factoring Shape, Pose, and Layout From the 2D Image of a 3D Scene | [
"Shubham Tulsiani",
"Saurabh Gupta",
"David F. Fouhey",
"Alexei A. Efros",
"Jitendra Malik"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Tulsiani_Factoring_Shape_Pose_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Tulsiani_Factoring_Shape_Pose_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0757-supp.pdf | 1712.01812 | cvf | @InProceedings{Tulsiani_2018_CVPR,author = {Tulsiani, Shubham and Gupta, Saurabh and Fouhey, David F. and Efros, Alexei A. and Malik, Jitendra},title = {Factoring Shape, Pose, and Layout From the 2D Image of a 3D Scene},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},m... | The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in terms of a small set of factors: a layout representing the enclosing surfaces as well as a set of objects represented in terms of shape and pose. We propose a convolutional neural network-based approach to predict this repres... | [
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32 | Monocular Relative Depth Perception With Web Stereo Data Supervision | [
"Ke Xian",
"Chunhua Shen",
"Zhiguo Cao",
"Hao Lu",
"Yang Xiao",
"Ruibo Li",
"Zhenbo Luo"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Xian_Monocular_Relative_Depth_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Xian_Monocular_Relative_Depth_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Xian_2018_CVPR,author = {Xian, Ke and Shen, Chunhua and Cao, Zhiguo and Lu, Hao and Xiao, Yang and Li, Ruibo and Luo, Zhenbo},title = {Monocular Relative Depth Perception With Web Stereo Data Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},m... | In this paper we study the problem of monocular relative depth perception in the wild. We introduce a simple yet effective method to automatically generate dense relative depth annotations from web stereo images, and propose a new dataset that consists of diverse images as well as corresponding dense relative depth map... | [
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33 | Spline Error Weighting for Robust Visual-Inertial Fusion | [
"Hannes Ovrén",
"Per-Erik Forssén"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Ovren_Spline_Error_Weighting_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Ovren_Spline_Error_Weighting_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2138-supp.zip | arXiv:1804.04820 | cvf | @InProceedings{Ovrén_2018_CVPR,author = {Ovrén, Hannes and Forssén, Per-Erik},title = {Spline Error Weighting for Robust Visual-Inertial Fusion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In this paper we derive and test a probability-based weighting that can balance residuals of different types in spline fitting. In contrast to previous formulations, the proposed spline error weighting scheme also incorporates a prediction of the approximation error of the spline fit. We demonstrate the effectiveness o... | [
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34 | Single-Image Depth Estimation Based on Fourier Domain Analysis | [
"Jae-Han Lee",
"Minhyeok Heo",
"Kyung-Rae Kim",
"Chang-Su Kim"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Lee_Single-Image_Depth_Estimation_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Lee_Single-Image_Depth_Estimation_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2873-supp.pdf | null | null | @InProceedings{Lee_2018_CVPR,author = {Lee, Jae-Han and Heo, Minhyeok and Kim, Kyung-Rae and Kim, Chang-Su},title = {Single-Image Depth Estimation Based on Fourier Domain Analysis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis. First, we develop a convolutional neural network structure and propose a new loss function, called depth-balanced Euclidean loss, to train the network reliably for a wide range of depths. Then, we gene... | [
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35 | Unsupervised Learning of Monocular Depth Estimation and Visual Odometry With Deep Feature Reconstruction | [
"Huangying Zhan",
"Ravi Garg",
"Chamara Saroj Weerasekera",
"Kejie Li",
"Harsh Agarwal",
"Ian Reid"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhan_Unsupervised_Learning_of_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhan_Unsupervised_Learning_of_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/4186-supp.pdf | 1803.03893 | cvf | @InProceedings{Zhan_2018_CVPR,author = {Zhan, Huangying and Garg, Ravi and Weerasekera, Chamara Saroj and Li, Kejie and Agarwal, Harsh and Reid, Ian},title = {Unsupervised Learning of Monocular Depth Estimation and Visual Odometry With Deep Feature Reconstruction},booktitle = {Proceedings of the IEEE Conference on Comp... | Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recent approaches to single view depth estimation explore the possibility of learning without full supervision via minimizing photometric error. ... | [
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36 | Detect-and-Track: Efficient Pose Estimation in Videos | [
"Rohit Girdhar",
"Georgia Gkioxari",
"Lorenzo Torresani",
"Manohar Paluri",
"Du Tran"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Girdhar_Detect-and-Track_Efficient_Pose_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Girdhar_Detect-and-Track_Efficient_Pose_CVPR_2018_paper.pdf | null | arXiv:1712.09184 | cvf | @InProceedings{Girdhar_2018_CVPR,author = {Girdhar, Rohit and Gkioxari, Georgia and Torresani, Lorenzo and Paluri, Manohar and Tran, Du},title = {Detect-and-Track: Efficient Pose Estimation in Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection and video understanding. Our method operates in two-stages: keypoint estimation i... | [
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37 | Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors | [
"Xuanyi Dong",
"Shoou-I Yu",
"Xinshuo Weng",
"Shih-En Wei",
"Yi Yang",
"Yaser Sheikh"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Dong_Supervision-by-Registration_An_Unsupervised_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Dong_Supervision-by-Registration_An_Unsupervised_CVPR_2018_paper.pdf | null | arXiv:1807.00966 | cvf | @InProceedings{Dong_2018_CVPR,author = {Dong, Xuanyi and Yu, Shoou-I and Weng, Xinshuo and Wei, Shih-En and Yang, Yi and Sheikh, Yaser},title = {Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors},booktitle = {Proceedings of the IEEE Conference on Computer Vision... | In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. Interestingly, cohe... | [
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38 | Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-Identification | [
"Shuang Li",
"Slawomir Bak",
"Peter Carr",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Li_Diversity_Regularized_Spatiotemporal_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Diversity_Regularized_Spatiotemporal_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0312-supp.pdf | 1803.09882 | cvf | @InProceedings{Li_2018_CVPR,author = {Li, Shuang and Bak, Slawomir and Carr, Peter and Wang, Xiaogang},title = {Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | Video-based person re-identification matches video clips of people across non-overlapping cameras. Most existing methods tackle this problem by encoding each video frame in its entirety and computing an aggregate representation across all frames. In practice, people are often partially occluded, which can corrupt the e... | [
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39 | Style Aggregated Network for Facial Landmark Detection | [
"Xuanyi Dong",
"Yan Yan",
"Wanli Ouyang",
"Yi Yang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Dong_Style_Aggregated_Network_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Dong_Style_Aggregated_Network_CVPR_2018_paper.pdf | null | 1803.04108 | cvf | @InProceedings{Dong_2018_CVPR,author = {Dong, Xuanyi and Yan, Yan and Ouyang, Wanli and Yang, Yi},title = {Style Aggregated Network for Facial Landmark Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Recent advances in facial landmark detection achieve success by learning discriminative features from rich deformation of face shapes and poses. Besides the variance of faces themselves, the intrinsic variance of image styles, e.g., grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has constantly... | [
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40 | Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision | [
"Yaojie Liu",
"Amin Jourabloo",
"Xiaoming Liu"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Learning_Deep_Models_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Learning_Deep_Models_CVPR_2018_paper.pdf | null | arXiv:1803.11097 | cvf | @InProceedings{Liu_2018_CVPR,author = {Liu, Yaojie and Jourabloo, Amin and Liu, Xiaoming},title = {Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary s... | [
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41 | Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation | [
"Kai Li",
"Junliang Xing",
"Chi Su",
"Weiming Hu",
"Yundong Zhang",
"Stephen Maybank"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Li_Deep_Cost-Sensitive_and_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Deep_Cost-Sensitive_and_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Li_2018_CVPR,author = {Li, Kai and Xing, Junliang and Su, Chi and Hu, Weiming and Zhang, Yundong and Maybank, Stephen},title = {Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Rec... | Facial age estimation from a face image is an important yet very challenging task in computer vision, since humans with different races and/or genders, exhibit quite different patterns in their facial aging processes. To deal with the influence of race and gender, previous methods perform age estimation within each pop... | [
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42 | First-Person Hand Action Benchmark With RGB-D Videos and 3D Hand Pose Annotations | [
"Guillermo Garcia-Hernando",
"Shanxin Yuan",
"Seungryul Baek",
"Tae-Kyun Kim"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Garcia-Hernando_First-Person_Hand_Action_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Garcia-Hernando_First-Person_Hand_Action_CVPR_2018_paper.pdf | null | arXiv:1704.02463 | cvf | @InProceedings{Garcia-Hernando_2018_CVPR,author = {Garcia-Hernando, Guillermo and Yuan, Shanxin and Baek, Seungryul and Kim, Tae-Kyun},title = {First-Person Hand Action Benchmark With RGB-D Videos and 3D Hand Pose Annotations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (C... | In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. To obtai... | [
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43 | A Pose-Sensitive Embedding for Person Re-Identification With Expanded Cross Neighborhood Re-Ranking | [
"M. Saquib Sarfraz",
"Arne Schumann",
"Andreas Eberle",
"Rainer Stiefelhagen"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Sarfraz_A_Pose-Sensitive_Embedding_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Sarfraz_A_Pose-Sensitive_Embedding_CVPR_2018_paper.pdf | null | 1711.10378 | cvf | @InProceedings{Sarfraz_2018_CVPR,author = {Sarfraz, M. Saquib and Schumann, Arne and Eberle, Andreas and Stiefelhagen, Rainer},title = {A Pose-Sensitive Embedding for Person Re-Identification With Expanded Cross Neighborhood Re-Ranking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Reco... | Person re-identification is a challenging retrieval task that requires matching a person’s acquired image across non-overlapping camera views. In this paper we propose an effective approach that incorporates both the fine and coarse pose information of the person to learn a discrim- inative embedding. In contrast to th... | [
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44 | Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment | [
"Amit Kumar",
"Rama Chellappa"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Kumar_Disentangling_3D_Pose_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Kumar_Disentangling_3D_Pose_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1230-supp.pdf | 1802.06713 | cvf | @InProceedings{Kumar_2018_CVPR,author = {Kumar, Amit and Chellappa, Rama},title = {Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendri... | [
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45 | A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation | [
"Kang Wang",
"Rui Zhao",
"Qiang Ji"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_A_Hierarchical_Generative_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_A_Hierarchical_Generative_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Wang_2018_CVPR,author = {Wang, Kang and Zhao, Rui and Ji, Qiang},title = {A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In this work, we introduce a Hierarchical Generative Model (HGM) to enable realistic forward eye image synthe- sis, as well as effective backward eye gaze estimation. The proposed HGM consists of a hierarchical generative shape model (HGSM), and a conditional bidirectional generative adversarial network (c-BiGAN). The ... | [
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46 | MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition | [
"Yizhou Zhou",
"Xiaoyan Sun",
"Zheng-Jun Zha",
"Wenjun Zeng"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_MiCT_Mixed_3D2D_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_MiCT_Mixed_3D2D_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Zhou_2018_CVPR,author = {Zhou, Yizhou and Sun, Xiaoyan and Zha, Zheng-Jun and Zeng, Wenjun},title = {MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Human actions in videos are three-dimensional (3D) signals. Recent attempts use 3D convolutional neural networks (CNNs) to explore spatio-temporal information for human action recognition. Though promising, 3D CNNs have not achieved high performanceon on this task with respect to their well-established two-dimensional ... | [
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47 | Learning to Estimate 3D Human Pose and Shape From a Single Color Image | [
"Georgios Pavlakos",
"Luyang Zhu",
"Xiaowei Zhou",
"Kostas Daniilidis"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Pavlakos_Learning_to_Estimate_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Pavlakos_Learning_to_Estimate_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3736-supp.pdf | 1805.04092 | cvf | @InProceedings{Pavlakos_2018_CVPR,author = {Pavlakos, Georgios and Zhu, Luyang and Zhou, Xiaowei and Daniilidis, Kostas},title = {Learning to Estimate 3D Human Pose and Shape From a Single Color Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | This work addresses the problem of estimating the full body 3D human pose and shape from a single color image. This is a task where iterative optimization-based solutions have typically prevailed, while Convolutional Networks (ConvNets) have suffered because of the lack of training data and their low resolution 3D pred... | [
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48 | Glimpse Clouds: Human Activity Recognition From Unstructured Feature Points | [
"Fabien Baradel",
"Christian Wolf",
"Julien Mille",
"Graham W. Taylor"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Baradel_Glimpse_Clouds_Human_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Baradel_Glimpse_Clouds_Human_CVPR_2018_paper.pdf | null | 1802.07898 | cvf | @InProceedings{Baradel_2018_CVPR,author = {Baradel, Fabien and Wolf, Christian and Mille, Julien and Taylor, Graham W.},title = {Glimpse Clouds: Human Activity Recognition From Unstructured Feature Points},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | We propose a method for human activity recognition from RGB data that does not rely on any pose information during test time, and does not explicitly calculate pose information internally. Instead, a visual attention module learns to predict glimpse sequences in each frame. These glimpses correspond to interest points ... | [
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49 | Context-Aware Deep Feature Compression for High-Speed Visual Tracking | [
"Jongwon Choi",
"Hyung Jin Chang",
"Tobias Fischer",
"Sangdoo Yun",
"Kyuewang Lee",
"Jiyeoup Jeong",
"Yiannis Demiris",
"Jin Young Choi"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Choi_Context-Aware_Deep_Feature_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_Context-Aware_Deep_Feature_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0892-supp.pdf | 1803.10537 | cvf | @InProceedings{Choi_2018_CVPR,author = {Choi, Jongwon and Chang, Hyung Jin and Fischer, Tobias and Yun, Sangdoo and Lee, Kyuewang and Jeong, Jiyeoup and Demiris, Yiannis and Choi, Jin Young},title = {Context-Aware Deep Feature Compression for High-Speed Visual Tracking},booktitle = {Proceedings of the IEEE Conference o... | We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme u... | [
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50 | Correlation Tracking via Joint Discrimination and Reliability Learning | [
"Chong Sun",
"Dong Wang",
"Huchuan Lu",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Sun_Correlation_Tracking_via_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Sun_Correlation_Tracking_via_CVPR_2018_paper.pdf | null | 1804.08965 | cvf | @InProceedings{Sun_2018_CVPR,author = {Sun, Chong and Wang, Dong and Lu, Huchuan and Yang, Ming-Hsuan},title = {Correlation Tracking via Joint Discrimination and Reliability Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected sal... | [
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51 | PhaseNet for Video Frame Interpolation | [
"Simone Meyer",
"Abdelaziz Djelouah",
"Brian McWilliams",
"Alexander Sorkine-Hornung",
"Markus Gross",
"Christopher Schroers"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Meyer_PhaseNet_for_Video_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Meyer_PhaseNet_for_Video_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1790-supp.pdf | 1804.00884 | cvf | @InProceedings{Meyer_2018_CVPR,author = {Meyer, Simone and Djelouah, Abdelaziz and McWilliams, Brian and Sorkine-Hornung, Alexander and Gross, Markus and Schroers, Christopher},title = {PhaseNet for Video Frame Interpolation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CV... | Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios with e.g. lighting changes or motion blur. Recent deep learning approaches that rely on kernels to represent motion can only alleviate thes... | [
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52 | The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation | [
"Pia Bideau",
"Aruni RoyChowdhury",
"Rakesh R. Menon",
"Erik Learned-Miller"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Bideau_The_Best_of_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Bideau_The_Best_of_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0569-supp.pdf | null | null | @InProceedings{Bideau_2018_CVPR,author = {Bideau, Pia and RoyChowdhury, Aruni and Menon, Rakesh R. and Learned-Miller, Erik},title = {The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Rec... | Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding. Modern CNN methods of motion analysis, on the other hand, excel at identifying well-known structures, but may not precisely characterize well-known ge... | [
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53 | Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning | [
"Xingping Dong",
"Jianbing Shen",
"Wenguan Wang",
"Yu Liu",
"Ling Shao",
"Fatih Porikli"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Dong_Hyperparameter_Optimization_for_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Dong_Hyperparameter_Optimization_for_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Dong_2018_CVPR,author = {Dong, Xingping and Shen, Jianbing and Wang, Wenguan and Liu, Yu and Shao, Ling and Porikli, Fatih},title = {Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}... | Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequen... | [
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54 | Scale-Transferrable Object Detection | [
"Peng Zhou",
"Bingbing Ni",
"Cong Geng",
"Jianguo Hu",
"Yi Xu"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Scale-Transferrable_Object_Detection_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Scale-Transferrable_Object_Detection_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Zhou_2018_CVPR,author = {Zhou, Peng and Ni, Bingbing and Geng, Cong and Hu, Jianguo and Xu, Yi},title = {Scale-Transferrable Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Scale problem lies in the heart of object detection. In this work, we develop a novel Scale-Transferrable Detection Network (STDN) for detecting multi-scale objects in images. In contrast to previous methods that simply combine object predictions from multiple feature maps from different network depths, the proposed ne... | [
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55 | A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos | [
"Chung-Ching Lin",
"Ying Hung"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Lin_A_Prior-Less_Method_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_A_Prior-Less_Method_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3502-supp.pdf | null | null | @InProceedings{Lin_2018_CVPR,author = {Lin, Chung-Ching and Hung, Ying},title = {A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | This paper presents a prior-less method for tracking and clustering an unknown number of human faces and maintaining their individual identities in unconstrained videos. The key challenge is to accurately track faces with partial occlusion and drastic appearance changes in multiple shots resulting from significant vari... | [
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56 | End-to-End Flow Correlation Tracking With Spatial-Temporal Attention | [
"Zheng Zhu",
"Wei Wu",
"Wei Zou",
"Junjie Yan"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhu_End-to-End_Flow_Correlation_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhu_End-to-End_Flow_Correlation_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1264-supp.pdf | 1711.01124 | cvf | @InProceedings{Zhu_2018_CVPR,author = {Zhu, Zheng and Wu, Wei and Zou, Wei and Yan, Junjie},title = {End-to-End Flow Correlation Tracking With Spatial-Temporal Attention},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks. However, most of existing DCF trackers only consider appearance features of current frame, and hardly benefit from motion and inter-frame information. The lack of temporal informa... | [
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57 | Deep Texture Manifold for Ground Terrain Recognition | [
"Jia Xue",
"Hang Zhang",
"Kristin Dana"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Xue_Deep_Texture_Manifold_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Xue_Deep_Texture_Manifold_CVPR_2018_paper.pdf | null | 1803.10896 | cvf | @InProceedings{Xue_2018_CVPR,author = {Xue, Jia and Zhang, Hang and Dana, Kristin},title = {Deep Texture Manifold for Ground Terrain Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orde... | [
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58 | Learning Superpixels With Segmentation-Aware Affinity Loss | [
"Wei-Chih Tu",
"Ming-Yu Liu",
"Varun Jampani",
"Deqing Sun",
"Shao-Yi Chien",
"Ming-Hsuan Yang",
"Jan Kautz"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Tu_Learning_Superpixels_With_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Tu_Learning_Superpixels_With_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0102-supp.pdf | null | null | @InProceedings{Tu_2018_CVPR,author = {Tu, Wei-Chih and Liu, Ming-Yu and Jampani, Varun and Sun, Deqing and Chien, Shao-Yi and Yang, Ming-Hsuan and Kautz, Jan},title = {Learning Superpixels With Segmentation-Aware Affinity Loss},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (... | Superpixel segmentation has been widely used in many computer vision tasks. Existing superpixel algorithms are mainly based on hand-crafted features, which often fail to preserve weak object boundaries. In this work, we leverage deep neural networks to facilitate extracting superpixels from images. We show a simple int... | [
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59 | Interactive Image Segmentation With Latent Diversity | [
"Zhuwen Li",
"Qifeng Chen",
"Vladlen Koltun"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Li_Interactive_Image_Segmentation_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Interactive_Image_Segmentation_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Li_2018_CVPR,author = {Li, Zhuwen and Chen, Qifeng and Koltun, Vladlen},title = {Interactive Image Segmentation With Latent Diversity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Interactive image segmentation is characterized by multimodality. When the user clicks on a door, do they intend to select the door or the whole house? We present an end-to-end learning approach to interactive image segmentation that tackles this ambiguity. Our architecture couples two convolutional networks. The first... | [
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60 | The Unreasonable Effectiveness of Deep Features as a Perceptual Metric | [
"Richard Zhang",
"Phillip Isola",
"Alexei A. Efros",
"Eli Shechtman",
"Oliver Wang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_The_Unreasonable_Effectiveness_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_The_Unreasonable_Effectiveness_CVPR_2018_paper.pdf | null | 1801.03924 | cvf | @InProceedings{Zhang_2018_CVPR,author = {Zhang, Richard and Isola, Phillip and Efros, Alexei A. and Shechtman, Eli and Wang, Oliver},title = {The Unreasonable Effectiveness of Deep Features as a Perceptual Metric},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of h... | [
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61 | Local Descriptors Optimized for Average Precision | [
"Kun He",
"Yan Lu",
"Stan Sclaroff"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/He_Local_Descriptors_Optimized_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/He_Local_Descriptors_Optimized_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0368-supp.pdf | 1804.05312 | cvf | @InProceedings{He_2018_CVPR,author = {He, Kun and Lu, Yan and Sclaroff, Stan},title = {Local Descriptors Optimized for Average Precision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper, we improve the learning of local feature descriptors by optimizi... | [
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62 | Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform | [
"Xintao Wang",
"Ke Yu",
"Chao Dong",
"Chen Change Loy"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Recovering_Realistic_Texture_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Recovering_Realistic_Texture_CVPR_2018_paper.pdf | null | 1804.02815 | cvf | @InProceedings{Wang_2018_CVPR,author = {Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},title = {Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem. In this paper, we show that it is possible to recover textures faithful to semantic classes. In particul... | [
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63 | Deep Extreme Cut: From Extreme Points to Object Segmentation | [
"Kevis-Kokitsi Maninis",
"Sergi Caelles",
"Jordi Pont-Tuset",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Maninis_Deep_Extreme_Cut_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Maninis_Deep_Extreme_Cut_CVPR_2018_paper.pdf | null | arXiv:1711.09081 | cvf | @InProceedings{Maninis_2018_CVPR,author = {Maninis, Kevis-Kokitsi and Caelles, Sergi and Pont-Tuset, Jordi and Van Gool, Luc},title = {Deep Extreme Cut: From Extreme Points to Object Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2... | This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do so by adding an extra channel to the image in the input of a convolutional neural network (CNN), which contains a Gaussian centered in each o... | [
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64 | Learning to Parse Wireframes in Images of Man-Made Environments | [
"Kun Huang",
"Yifan Wang",
"Zihan Zhou",
"Tianjiao Ding",
"Shenghua Gao",
"Yi Ma"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Learning_to_Parse_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Learning_to_Parse_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1446-supp.pdf | 2007.07527 | cvf | @InProceedings{Huang_2018_CVPR,author = {Huang, Kun and Wang, Yifan and Zhou, Zihan and Ding, Tianjiao and Gao, Shenghua and Ma, Yi},title = {Learning to Parse Wireframes in Images of Man-Made Environments},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June}... | In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. The wireframe contains all salient straight lines and their junctions of the scene that encode efficiently and accurately large-scale geometry and objec... | [
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65 | Occlusion-Aware Rolling Shutter Rectification of 3D Scenes | [
"Subeesh Vasu",
"Mahesh Mohan M. R.",
"A. N. Rajagopalan"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Vasu_Occlusion-Aware_Rolling_Shutter_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Vasu_Occlusion-Aware_Rolling_Shutter_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1800-supp.pdf | null | null | @InProceedings{Vasu_2018_CVPR,author = {Vasu, Subeesh and R., Mahesh Mohan M. and Rajagopalan, A. N.},title = {Occlusion-Aware Rolling Shutter Rectification of 3D Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | A vast majority of contemporary cameras employ rolling shutter (RS) mechanism to capture images. Due to the sequential mechanism, images acquired with a moving camera are subjected to rolling shutter effect which manifests as geometric distortions. In this work, we consider the specific scenario of a fast moving camera... | [
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66 | Content-Sensitive Supervoxels via Uniform Tessellations on Video Manifolds | [
"Ran Yi",
"Yong-Jin Liu",
"Yu-Kun Lai"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yi_Content-Sensitive_Supervoxels_via_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yi_Content-Sensitive_Supervoxels_via_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2102-supp.pdf | null | null | @InProceedings{Yi_2018_CVPR,author = {Yi, Ran and Liu, Yong-Jin and Lai, Yu-Kun},title = {Content-Sensitive Supervoxels via Uniform Tessellations on Video Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Supervoxels are perceptually meaningful atomic regions in videos, obtained by grouping voxels that exhibit coherence in both appearance and motion. In this paper, we propose content-sensitive supervoxels (CSS), which are regularly-shaped 3D primitive volumes that possess the following characteristic: they are typically... | [
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67 | Intrinsic Image Transformation via Scale Space Decomposition | [
"Lechao Cheng",
"Chengyi Zhang",
"Zicheng Liao"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.pdf | null | 1805.10253 | cvf | @InProceedings{Cheng_2018_CVPR,author = {Cheng, Lechao and Zhang, Chengyi and Liao, Zicheng},title = {Intrinsic Image Transformation via Scale Space Decomposition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We introduce a new network structure for decomposing an image into its intrinsic albedo and shading. We treat this as an image-to-image transformation problem and explore the scale space of the input and output. By expanding the output images (albedo and shading) into their Laplacian pyramid components, we develop a mu... | [
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68 | Learned Shape-Tailored Descriptors for Segmentation | [
"Naeemullah Khan",
"Ganesh Sundaramoorthi"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Khan_Learned_Shape-Tailored_Descriptors_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Khan_Learned_Shape-Tailored_Descriptors_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/2834-supp.pdf | null | null | @InProceedings{Khan_2018_CVPR,author = {Khan, Naeemullah and Sundaramoorthi, Ganesh},title = {Learned Shape-Tailored Descriptors for Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We address the problem of texture segmentation by grouping dense pixel-wise descriptors. We introduce and construct learned Shape-Tailored Descriptors that aggregate image statistics only within regions of interest to avoid mixing statistics of different textures, and that are invariant to complex nuisances (e.g., illu... | [
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69 | PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing | [
"Dan Xu",
"Wanli Ouyang",
"Xiaogang Wang",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Xu_PAD-Net_Multi-Tasks_Guided_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_PAD-Net_Multi-Tasks_Guided_CVPR_2018_paper.pdf | null | arXiv:1805.04409 | cvf | @InProceedings{Xu_2018_CVPR,author = {Xu, Dan and Ouyang, Wanli and Wang, Xiaogang and Sebe, Nicu},title = {PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVP... | Depth estimation and scene parsing are two particularly important tasks in visual scene understanding. In this paper we tackle the problem of simultaneous depth estimation and scene parsing in a joint CNN. The task can be typically treated as a deep multi-task learning problem [42]. Different from previous methods dire... | [
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70 | Multi-Image Semantic Matching by Mining Consistent Features | [
"Qianqian Wang",
"Xiaowei Zhou",
"Kostas Daniilidis"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Multi-Image_Semantic_Matching_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Multi-Image_Semantic_Matching_CVPR_2018_paper.pdf | null | 1711.07641 | cvf | @InProceedings{Wang_2018_CVPR,author = {Wang, Qianqian and Zhou, Xiaowei and Daniilidis, Kostas},title = {Multi-Image Semantic Matching by Mining Consistent Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the propo... | [
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71 | Density-Aware Single Image De-Raining Using a Multi-Stream Dense Network | [
"He Zhang",
"Vishal M. Patel"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Density-Aware_Single_Image_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Density-Aware_Single_Image_CVPR_2018_paper.pdf | null | 1802.07412 | cvf | @InProceedings{Zhang_2018_CVPR,author = {Zhang, He and Patel, Vishal M.},title = {Density-Aware Single Image De-Raining Using a Multi-Stream Dense Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Single image rain streak removal is an extremely challenging problem due to the presence of non-uniform rain densities in images. We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed m... | [
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72 | Joint Cuts and Matching of Partitions in One Graph | [
"Tianshu Yu",
"Junchi Yan",
"Jieyi Zhao",
"Baoxin Li"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Joint_Cuts_and_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Joint_Cuts_and_CVPR_2018_paper.pdf | null | 1711.09584 | cvf | @InProceedings{Yu_2018_CVPR,author = {Yu, Tianshu and Yan, Junchi and Zhao, Jieyi and Li, Baoxin},title = {Joint Cuts and Matching of Partitions in One Graph},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | As two fundamental problems, graph cuts and graph matching have been intensively investigated over the decades, resulting in vast literature in these two topics respectively. However the way of jointly applying and solving graph cuts and matching receives few attention. In this paper, we first formalize the problem of ... | [
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73 | Progressive Attention Guided Recurrent Network for Salient Object Detection | [
"Xiaoning Zhang",
"Tiantian Wang",
"Jinqing Qi",
"Huchuan Lu",
"Gang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Progressive_Attention_Guided_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Progressive_Attention_Guided_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Zhang_2018_CVPR,author = {Zhang, Xiaoning and Wang, Tiantian and Qi, Jinqing and Lu, Huchuan and Wang, Gang},title = {Progressive Attention Guided Recurrent Network for Salient Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | Effective convolutional features play an important role in saliency estimation but how to learn powerful features for saliency is still a challenging task. FCN-based methods directly apply multi-level convolutional features without distinction, which leads to sub-optimal results due to the distraction from redundant de... | [
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74 | Fast and Accurate Single Image Super-Resolution via Information Distillation Network | [
"Zheng Hui",
"Xiumei Wang",
"Xinbo Gao"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Hui_Fast_and_Accurate_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Hui_Fast_and_Accurate_CVPR_2018_paper.pdf | null | 1803.09454 | cvf | @InProceedings{Hui_2018_CVPR,author = {Hui, Zheng and Wang, Xiumei and Gao, Xinbo},title = {Fast and Accurate Single Image Super-Resolution via Information Distillation Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Recently, deep convolutional neural networks (CNNs) have been demonstrated remarkable progress on single image super-resolution. However, as the depth and width of the networks increase, CNN-based super-resolution methods have been faced with the challenges of computational complexity and memory consumption in practice... | [
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75 | Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning | [
"Kwan-Yee Lin",
"Guanxiang Wang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Lin_Hallucinated-IQA_No-Reference_Image_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_Hallucinated-IQA_No-Reference_Image_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1335-supp.pdf | arXiv:1804.01681 | cvf | @InProceedings{Lin_2018_CVPR,author = {Lin, Kwan-Yee and Wang, Guanxiang},title = {Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms hav... | [
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76 | NAG: Network for Adversary Generation | [
"Konda Reddy Mopuri",
"Utkarsh Ojha",
"Utsav Garg",
"R. Venkatesh Babu"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Mopuri_NAG_Network_for_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Mopuri_NAG_Network_for_CVPR_2018_paper.pdf | null | 1712.03390 | cvf | @InProceedings{Mopuri_2018_CVPR,author = {Mopuri, Konda Reddy and Ojha, Utkarsh and Garg, Utsav and Babu, R. Venkatesh},title = {NAG: Network for Adversary Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility c... | [
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77 | Dynamic-Structured Semantic Propagation Network | [
"Xiaodan Liang",
"Hongfei Zhou",
"Eric Xing"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Liang_Dynamic-Structured_Semantic_Propagation_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Liang_Dynamic-Structured_Semantic_Propagation_CVPR_2018_paper.pdf | null | 1803.06067 | cvf | @InProceedings{Liang_2018_CVPR,author = {Liang, Xiaodan and Zhou, Hongfei and Xing, Eric},title = {Dynamic-Structured Semantic Propagation Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Semantic concept hierarchy is yet under-explored for semantic segmentation due to the inefficiency and complicated optimization of incorporating structural inference into the dense prediction. This lack of modeling dependencies among concepts severely limits the generalization capability of segmentation models for open... | [
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78 | Cross-Domain Self-Supervised Multi-Task Feature Learning Using Synthetic Imagery | [
"Zhongzheng Ren",
"Yong Jae Lee"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Ren_Cross-Domain_Self-Supervised_Multi-Task_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Ren_Cross-Domain_Self-Supervised_Multi-Task_CVPR_2018_paper.pdf | null | 1711.09082 | cvf | @InProceedings{Ren_2018_CVPR,author = {Ren, Zhongzheng and Lee, Yong Jae},title = {Cross-Domain Self-Supervised Multi-Task Feature Learning Using Synthetic Imagery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In human learning, it is common to use multiple sources of information jointly. However, most existing feature learning approaches learn from only a single task. In this paper, we propose a novel multi-task deep network to learn generalizable high-level visual representations. Since multi-task learning requires annotat... | [
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79 | A Two-Step Disentanglement Method | [
"Naama Hadad",
"Lior Wolf",
"Moni Shahar"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Hadad_A_Two-Step_Disentanglement_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Hadad_A_Two-Step_Disentanglement_CVPR_2018_paper.pdf | null | 1709.00199 | cvf | @InProceedings{Hadad_2018_CVPR,author = {Hadad, Naama and Wolf, Lior and Shahar, Moni},title = {A Two-Step Disentanglement Method},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training. First, the part of the data that is correlated with the labels is extracted by traini... | [
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80 | Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network | [
"Daniel Merget",
"Matthias Rock",
"Gerhard Rigoll"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Merget_Robust_Facial_Landmark_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Merget_Robust_Facial_Landmark_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Merget_2018_CVPR,author = {Merget, Daniel and Rock, Matthias and Rigoll, Gerhard},title = {Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {201... | While fully-convolutional neural networks are very strong at modeling local features, they fail to aggregate global context due to their constrained receptive field. Modern methods typically address the lack of global context by introducing cascades, pooling, or by fitting a statistical model. In this work, we propose ... | [
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81 | Decorrelated Batch Normalization | [
"Lei Huang",
"Dawei Yang",
"Bo Lang",
"Jia Deng"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Decorrelated_Batch_Normalization_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Decorrelated_Batch_Normalization_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1134-supp.pdf | 1804.08450 | cvf | @InProceedings{Huang_2018_CVPR,author = {Huang, Lei and Yang, Dawei and Lang, Bo and Deng, Jia},title = {Decorrelated Batch Normalization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find th... | [
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82 | Learning to Sketch With Shortcut Cycle Consistency | [
"Jifei Song",
"Kaiyue Pang",
"Yi-Zhe Song",
"Tao Xiang",
"Timothy M. Hospedales"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Song_Learning_to_Sketch_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Song_Learning_to_Sketch_CVPR_2018_paper.pdf | null | 1805.00247 | cvf | @InProceedings{Song_2018_CVPR,author = {Song, Jifei and Pang, Kaiyue and Song, Yi-Zhe and Xiang, Tao and Hospedales, Timothy M.},title = {Learning to Sketch With Shortcut Cycle Consistency},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | To see is to sketch -- free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process. This is an extremely challenging task because the photo and sketch domains differ significantl... | [
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83 | Towards a Mathematical Understanding of the Difficulty in Learning With Feedforward Neural Networks | [
"Hao Shen"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Shen_Towards_a_Mathematical_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_Towards_a_Mathematical_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1462-supp.pdf | 1611.05827 | cvf | @InProceedings{Shen_2018_CVPR,author = {Shen, Hao},title = {Towards a Mathematical Understanding of the Difficulty in Learning With Feedforward Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Training deep neural networks for solving machine learning problems is one great challenge in the field, mainly due to its associated optimisation problem being highly non-convex. Recent developments have suggested that many training algorithms do not suffer from undesired local minima under certain scenario, and conse... | [
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84 | FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis | [
"Yujun Shen",
"Ping Luo",
"Junjie Yan",
"Xiaogang Wang",
"Xiaoou Tang"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Shen_FaceID-GAN_Learning_a_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_FaceID-GAN_Learning_a_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Shen_2018_CVPR,author = {Shen, Yujun and Luo, Ping and Yan, Junjie and Wang, Xiaogang and Tang, Xiaoou},title = {FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mont... | Face synthesis has achieved advanced development by using generative adversarial networks (GANs). Existing methods typically formulate GAN as a two-player game, where a discriminator distinguishes face images from the real and synthesized domains, while a generator reduces its discriminativeness by synthesizing a face ... | [
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85 | A Constrained Deep Neural Network for Ordinal Regression | [
"Yanzhu Liu",
"Adams Wai Kin Kong",
"Chi Keong Goh"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Liu_A_Constrained_Deep_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_A_Constrained_Deep_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Liu_2018_CVPR,author = {Liu, Yanzhu and Kong, Adams Wai Kin and Goh, Chi Keong},title = {A Constrained Deep Neural Network for Ordinal Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Ordinal regression is a supervised learning problem aiming to classify instances into ordinal categories. It is challenging to automatically extract high-level features for representing intraclass information and interclass ordinal relationship simultaneously. This paper proposes a constrained optimization formulation ... | [
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86 | Modulated Convolutional Networks | [
"Xiaodi Wang",
"Baochang Zhang",
"Ce Li",
"Rongrong Ji",
"Jungong Han",
"Xianbin Cao",
"Jianzhuang Liu"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Modulated_Convolutional_Networks_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Modulated_Convolutional_Networks_CVPR_2018_paper.pdf | null | arXiv:1804.00227 | cvf | @InProceedings{Wang_2018_CVPR,author = {Wang, Xiaodi and Zhang, Baochang and Li, Ce and Ji, Rongrong and Han, Jungong and Cao, Xianbin and Liu, Jianzhuang},title = {Modulated Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | Despite great effectiveness of very deep and wide Convolutional Neural Networks (CNNs) in various computer vision tasks, the significant cost in terms of storage requirement of such networks impedes the deployment on computationally limited devices. In this paper, we propose new Modulated Convolutional Networks (MCNs) ... | [
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87 | Learning Steerable Filters for Rotation Equivariant CNNs | [
"Maurice Weiler",
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"Martin Storath"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Weiler_Learning_Steerable_Filters_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Weiler_Learning_Steerable_Filters_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3214-supp.pdf | 1711.07289 | cvf | @InProceedings{Weiler_2018_CVPR,author = {Weiler, Maurice and Hamprecht, Fred A. and Storath, Martin},title = {Learning Steerable Filters for Rotation Equivariant CNNs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In ... | [
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88 | Efficient Interactive Annotation of Segmentation Datasets With Polygon-RNN++ | [
"David Acuna",
"Huan Ling",
"Amlan Kar",
"Sanja Fidler"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Acuna_Efficient_Interactive_Annotation_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Acuna_Efficient_Interactive_Annotation_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3409-supp.pdf | arXiv:1803.09693 | cvf | @InProceedings{Acuna_2018_CVPR,author = {Acuna, David and Ling, Huan and Kar, Amlan and Fidler, Sanja},title = {Efficient Interactive Annotation of Segmentation Datasets With Polygon-RNN++},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we design a new CNN encoder architecture, 2) show how... | [
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89 | SplineCNN: Fast Geometric Deep Learning With Continuous B-Spline Kernels | [
"Matthias Fey",
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"Frank Weichert",
"Heinrich Müller"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/3827-supp.pdf | arXiv:1711.08920 | cvf | @InProceedings{Fey_2018_CVPR,author = {Fey, Matthias and Lenssen, Jan Eric and Weichert, Frank and Müller, Heinrich},title = {SplineCNN: Fast Geometric Deep Learning With Continuous B-Spline Kernels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year =... | We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to th... | [
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90 | GAGAN: Geometry-Aware Generative Adversarial Networks | [
"Jean Kossaifi",
"Linh Tran",
"Yannis Panagakis",
"Maja Pantic"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Kossaifi_GAGAN_Geometry-Aware_Generative_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Kossaifi_GAGAN_Geometry-Aware_Generative_CVPR_2018_paper.pdf | null | 1712.00684 | cvf | @InProceedings{Kossaifi_2018_CVPR,author = {Kossaifi, Jean and Tran, Linh and Panagakis, Yannis and Pantic, Maja},title = {GAGAN: Geometry-Aware Generative Adversarial Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their texture, the visual appearance of objects is significantly influenced by their shape geometry; information which is not taken into account by ... | [
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91 | On the Robustness of Semantic Segmentation Models to Adversarial Attacks | [
"Anurag Arnab",
"Ondrej Miksik",
"Philip H.S. Torr"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Arnab_On_the_Robustness_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Arnab_On_the_Robustness_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0261-supp.pdf | 1711.09856 | cvf | @InProceedings{Arnab_2018_CVPR,author = {Arnab, Anurag and Miksik, Ondrej and Torr, Philip H.S.},title = {On the Robustness of Semantic Segmentation Models to Adversarial Attacks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally well on most recognition tasks such as image classification and segmentation. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively... | [
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92 | Feedback-Prop: Convolutional Neural Network Inference Under Partial Evidence | [
"Tianlu Wang",
"Kota Yamaguchi",
"Vicente Ordonez"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Feedback-Prop_Convolutional_Neural_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Feedback-Prop_Convolutional_Neural_CVPR_2018_paper.pdf | null | arXiv:1710.08049 | cvf | @InProceedings{Wang_2018_CVPR,author = {Wang, Tianlu and Yamaguchi, Kota and Ordonez, Vicente},title = {Feedback-Prop: Convolutional Neural Network Inference Under Partial Evidence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We propose an inference procedure for deep convolutional neural networks (CNNs) when partial evidence is available. Our method consists of a general feedback-based propagation approach (feedback-prop) that boosts the prediction accuracy for an arbitrary set of unknown target labels when the values for a non-overlapping... | [
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93 | Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes | [
"Xin Yu",
"Basura Fernando",
"Richard Hartley",
"Fatih Porikli"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Super-Resolving_Very_Low-Resolution_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Super-Resolving_Very_Low-Resolution_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Yu_2018_CVPR,author = {Yu, Xin and Fernando, Basura and Hartley, Richard and Porikli, Fatih},title = {Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {20... | Given a tiny face image, conventional face hallucination methods aim to super-resolve its high-resolution (HR) counterpart by learning a mapping from an exemplar dataset. Since a low-resolution (LR) input patch may correspond to many HR candidate patches, this ambiguity may lead to erroneous HR facial details and thus ... | [
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94 | Frustum PointNets for 3D Object Detection From RGB-D Data | [
"Charles R. Qi",
"Wei Liu",
"Chenxia Wu",
"Hao Su",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Qi_Frustum_PointNets_for_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0019-supp.pdf | arXiv:1711.08488 | cvf | @InProceedings{Qi_2018_CVPR,author = {Qi, Charles R. and Liu, Wei and Wu, Chenxia and Su, Hao and Guibas, Leonidas J.},title = {Frustum PointNets for 3D Object Detection From RGB-D Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach ... | [
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95 | W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection | [
"Yongqiang Zhang",
"Yancheng Bai",
"Mingli Ding",
"Yongqiang Li",
"Bernard Ghanem"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_W2F_A_Weakly-Supervised_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_W2F_A_Weakly-Supervised_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0165-supp.pdf | null | null | @InProceedings{Zhang_2018_CVPR,author = {Zhang, Yongqiang and Bai, Yancheng and Ding, Mingli and Li, Yongqiang and Ghanem, Bernard},title = {W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mon... | Weakly-supervised object detection has attracted much attention lately, since it does not require bounding box annotations for training. Although significant progress has also been made, there is still a large gap in performance between weakly-supervised and fully-supervised object detection. Recently, some works use p... | [
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96 | 3D Object Detection With Latent Support Surfaces | [
"Zhile Ren",
"Erik B. Sudderth"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Ren_3D_Object_Detection_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Ren_3D_Object_Detection_CVPR_2018_paper.pdf | null | null | null | @InProceedings{Ren_2018_CVPR,author = {Ren, Zhile and Sudderth, Erik B.},title = {3D Object Detection With Latent Support Surfaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes. Existing 3D representations for RGB-D images capture the local shape and appearance of object categories, but have limited power to represent objects with different visual styles. The detec... | [
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97 | Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization | [
"Peihua Li",
"Jiangtao Xie",
"Qilong Wang",
"Zilin Gao"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Li_Towards_Faster_Training_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Towards_Faster_Training_CVPR_2018_paper.pdf | null | 1712.01034 | cvf | @InProceedings{Li_2018_CVPR,author = {Li, Peihua and Xie, Jiangtao and Wang, Qilong and Gao, Zilin},title = {Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mon... | Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance. However, existing methods depend heavily on eigendecomposition (... | [
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98 | Recurrent Scene Parsing With Perspective Understanding in the Loop | [
"Shu Kong",
"Charless C. Fowlkes"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Kong_Recurrent_Scene_Parsing_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Kong_Recurrent_Scene_Parsing_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0534-supp.pdf | 1705.07238 | cvf | @InProceedings{Kong_2018_CVPR,author = {Kong, Shu and Fowlkes, Charless C.},title = {Recurrent Scene Parsing With Perspective Understanding in the Loop},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2018}} | Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inve... | [
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99 | Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors | [
"Junhyug Noh",
"Soochan Lee",
"Beomsu Kim",
"Gunhee Kim"
] | https://openaccess.thecvf.com/content_cvpr_2018/html/Noh_Improving_Occlusion_and_CVPR_2018_paper.html | https://openaccess.thecvf.com/content_cvpr_2018/papers/Noh_Improving_Occlusion_and_CVPR_2018_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2018/Supplemental/0620-supp.pdf | null | null | @InProceedings{Noh_2018_CVPR,author = {Noh, Junhyug and Lee, Soochan and Kim, Beomsu and Kim, Gunhee},title = {Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2... | We propose methods of addressing two critical issues of pedestrian detection: (i) occlusion of target objects as false negative failure, and (ii) confusion with hard negative examples like vertical structures as false positive failure. Our solutions to these two problems are general and flexible enough to be applicable... | [
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