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 0 782 | title stringlengths 6 154 | authors listlengths 1 16 | cvf_url stringlengths 89 130 | pdf_url stringlengths 90 131 | supp_url stringlengths 103 140 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 225 551 | abstract large_stringlengths 506 2.26k | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Graph-Structured Representations for Visual Question Answering | [
"Damien Teney",
"Lingqiao Liu",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Teney_Graph-Structured_Representations_for_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Teney_Graph-Structured_Representations_for_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Teney_Graph-Structured_Representations_for_2017_CVPR_supplemental.pdf | 1609.05600 | cvf | @InProceedings{Teney_2017_CVPR,author = {Teney, Damien and Liu, Lingqiao and van den Hengel, Anton},title = {Graph-Structured Representations for Visual Question Answering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reasoning over the visual and text domains. The predominant CNN/LSTM-based approach to VQA is limited by monolithic vector representations that ... | [
0.03675725683569908,
-0.026675034314393997,
0.01531275361776352,
0.08339757472276688,
0.011892475187778473,
0.040392883121967316,
0.007563861086964607,
0.01562623493373394,
-0.013578410260379314,
-0.02264740876853466,
-0.02511690929532051,
0.0024469858035445213,
-0.061269909143447876,
-0.0... |
1 | Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation | [
"Hao Zhao",
"Ming Lu",
"Anbang Yao",
"Yiwen Guo",
"Yurong Chen",
"Li Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Zhao_Physics_Inspired_Optimization_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Physics_Inspired_Optimization_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhao_Physics_Inspired_Optimization_2017_CVPR_supplemental.zip | 1707.00383 | cvf | @InProceedings{Zhao_2017_CVPR,author = {Zhao, Hao and Lu, Ming and Yao, Anbang and Guo, Yiwen and Chen, Yurong and Zhang, Li},title = {Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pat... | In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural... | [
0.00312134250998497,
-0.00884355790913105,
0.012549043633043766,
-0.0028494857251644135,
0.06404345482587814,
0.03362419828772545,
0.003626299323514104,
-0.0011250434909015894,
-0.012209571897983551,
-0.0380641371011734,
-0.041530508548021317,
-0.018334820866584778,
-0.045289915055036545,
... |
2 | Local Binary Convolutional Neural Networks | [
"Felix Juefei-Xu",
"Vishnu Naresh Boddeti",
"Marios Savvides"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Juefei-Xu_Local_Binary_Convolutional_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Juefei-Xu_Local_Binary_Convolutional_CVPR_2017_paper.pdf | null | 1608.06049 | cvf | @InProceedings{Juefei-Xu_2017_CVPR,author = {Juefei-Xu, Felix and Naresh Boddeti, Vishnu and Savvides, Marios},title = {Local Binary Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not u... | [
0.018494807183742523,
-0.035634007304906845,
0.019526224583387375,
0.030445635318756104,
0.02552463486790657,
0.02980736270546913,
-0.013007979840040207,
0.006620160769671202,
-0.024978114292025566,
-0.0485849604010582,
0.003148104064166546,
-0.01105531957000494,
-0.052632126957178116,
-0.... |
3 | Designing Effective Inter-Pixel Information Flow for Natural Image Matting | [
"Yagiz Aksoy",
"Tunc Ozan Aydin",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Aksoy_Designing_Effective_Inter-Pixel_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Aksoy_Designing_Effective_Inter-Pixel_CVPR_2017_paper.pdf | null | 1707.05055 | cvf | @InProceedings{Aksoy_2017_CVPR,author = {Aksoy, Yagiz and Ozan Aydin, Tunc and Pollefeys, Marc},title = {Designing Effective Inter-Pixel Information Flow for Natural Image Matting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | We present a novel, purely affinity-based natural image matting algorithm. Our method relies on carefully defined pixel-to-pixel connections that enable effective use of information available in the image and the trimap. We control the information flow from the known-opacity regions into the unknown region, as well as ... | [
0.03560356795787811,
-0.0022604360710829496,
0.005160589702427387,
0.030969008803367615,
0.016723396256566048,
0.016653303056955338,
0.013673407025635242,
0.009141691029071808,
-0.04013517498970032,
-0.06351024657487869,
-0.05101744085550308,
-0.010464947670698166,
-0.0685790553689003,
-0.... |
4 | Face Normals "In-The-Wild" Using Fully Convolutional Networks | [
"George Trigeorgis",
"Patrick Snape",
"Iasonas Kokkinos",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Trigeorgis_Face_Normals_In-The-Wild_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Trigeorgis_Face_Normals_In-The-Wild_CVPR_2017_paper.pdf | null | null | null | @InProceedings{Trigeorgis_2017_CVPR,author = {Trigeorgis, George and Snape, Patrick and Kokkinos, Iasonas and Zafeiriou, Stefanos},title = {Face Normals "In-The-Wild" Using Fully Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},yea... | In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the tas... | [
0.0019055877346545458,
-0.005005470011383295,
0.010274162515997887,
0.015952568501234055,
0.03341960161924362,
0.025484977290034294,
0.05340445786714554,
0.012662874534726143,
0.0037584854289889336,
-0.05807396024465561,
0.0018729388248175383,
0.006052774842828512,
-0.06496679037809372,
-0... |
5 | 3D Face Morphable Models "In-The-Wild" | [
"James Booth",
"Epameinondas Antonakos",
"Stylianos Ploumpis",
"George Trigeorgis",
"Yannis Panagakis",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Booth_3D_Face_Morphable_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Booth_3D_Face_Morphable_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Booth_3D_Face_Morphable_2017_CVPR_supplemental.pdf | 1701.05360 | cvf | @InProceedings{Booth_2017_CVPR,author = {Booth, James and Antonakos, Epameinondas and Ploumpis, Stylianos and Trigeorgis, George and Panagakis, Yannis and Zafeiriou, Stefanos},title = {3D Face Morphable Models "In-The-Wild"},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVP... | 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. Howev... | [
-0.00042638496961444616,
-0.008744193241000175,
-0.012486959807574749,
0.003296482376754284,
0.04476035758852959,
0.03407835215330124,
0.04885730519890785,
0.006255060434341431,
-0.03462151065468788,
-0.06723393499851227,
-0.03282606601715088,
-0.002936755074188113,
-0.0721268281340599,
0.... |
6 | Towards a Quality Metric for Dense Light Fields | [
"Vamsi Kiran Adhikarla",
"Marek Vinkler",
"Denis Sumin",
"Rafal K. Mantiuk",
"Karol Myszkowski",
"Hans-Peter Seidel",
"Piotr Didyk"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Adhikarla_Towards_a_Quality_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Adhikarla_Towards_a_Quality_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Adhikarla_Towards_a_Quality_2017_CVPR_supplemental.pdf | 1704.07576 | cvf | @InProceedings{Adhikarla_2017_CVPR,author = {Kiran Adhikarla, Vamsi and Vinkler, Marek and Sumin, Denis and Mantiuk, Rafal K. and Myszkowski, Karol and Seidel, Hans-Peter and Didyk, Piotr},title = {Towards a Quality Metric for Dense Light Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pa... | Light fields become a popular representation of three-dimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as w... | [
0.00822032056748867,
0.011965914629399776,
0.007489238400012255,
0.01654348336160183,
0.04784760996699333,
-0.006810980383306742,
-0.012774215079843998,
0.020589718595147133,
-0.023936182260513306,
-0.06236765906214714,
-0.03250677138566971,
0.0032992030028253794,
-0.052955642342567444,
0.... |
7 | Position Tracking for Virtual Reality Using Commodity WiFi | [
"Manikanta Kotaru",
"Sachin Katti"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Kotaru_Position_Tracking_for_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Kotaru_Position_Tracking_for_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Kotaru_Position_Tracking_for_2017_CVPR_supplemental.zip | 1703.03468 | cvf | @InProceedings{Kotaru_2017_CVPR,author = {Kotaru, Manikanta and Katti, Sachin},title = {Position Tracking for Virtual Reality Using Commodity WiFi},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {July},year = {2017}} | Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g., Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e.g., S... | [
0.013825745321810246,
0.002881023334339261,
0.028537273406982422,
0.05115377530455589,
0.0669015422463417,
0.008340224623680115,
0.03191954270005226,
0.029456092044711113,
-0.0034509138204157352,
-0.07747572660446167,
-0.04155842214822769,
-0.027418214827775955,
-0.0654301568865776,
-0.006... |
8 | Material Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion | [
"Kenichiro Tanaka",
"Yasuhiro Mukaigawa",
"Takuya Funatomi",
"Hiroyuki Kubo",
"Yasuyuki Matsushita",
"Yasushi Yagi"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Tanaka_Material_Classification_Using_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Tanaka_Material_Classification_Using_CVPR_2017_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2017/supplemental/Tanaka_Material_Classification_Using_2017_CVPR_supplemental.pdf | null | null | @InProceedings{Tanaka_2017_CVPR,author = {Tanaka, Kenichiro and Mukaigawa, Yasuhiro and Funatomi, Takuya and Kubo, Hiroyuki and Matsushita, Yasuyuki and Yagi, Yasushi},title = {Material Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion},booktitle = {Proceedings of the IEEE Conference on Comp... | This paper presents a material classification method using an off-the-shelf Time-of-Flight (ToF) camera. We use a key observation that the depth measurement by a ToF camera is distorted in objects with certain materials, especially with translucent materials. We show that this distortion is caused by the variations of ... | [
0.029514092952013016,
0.020465003326535225,
-0.017587391659617424,
0.01995697058737278,
0.061706285923719406,
0.0278796274214983,
-0.00020383464288897812,
0.02628915384411812,
-0.021745795384049416,
-0.048628926277160645,
-0.014906792901456356,
0.021060314029455185,
-0.012157184071838856,
... |
9 | Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks | [
"Philip Haeusser",
"Alexander Mordvintsev",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_cvpr_2017/html/Haeusser_Learning_by_Association_CVPR_2017_paper.html | https://openaccess.thecvf.com/content_cvpr_2017/papers/Haeusser_Learning_by_Association_CVPR_2017_paper.pdf | null | 1706.00909 | title_snapshot | @InProceedings{Haeusser_2017_CVPR,author = {Haeusser, Philip and Mordvintsev, Alexander and Cremers, Daniel},title = {Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by lear... | [
0.01297180075198412,
-0.016038717702031136,
-0.020119940862059593,
0.04637077823281288,
0.035174667835235596,
0.014211015775799751,
0.007457116153091192,
-0.011416955851018429,
-0.028698761016130447,
-0.026227576658129692,
-0.03701678663492203,
0.008570677600800991,
-0.06621222198009491,
-... |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 29