CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id uint32 0 978 | title stringlengths 13 151 | authors listlengths 1 24 | cvf_url stringlengths 88 127 | pdf_url stringlengths 89 128 | supp_url stringlengths 74 74 ⌀ | arxiv_id stringlengths 10 16 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 226 695 | abstract large_stringlengths 536 2.24k | embedding listlengths 768 768 |
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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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