paper_id uint32 | title string | authors list | cvf_url string | pdf_url string | supp_url string | arxiv_id string | arxiv_id_source string | bibtex large_string | abstract large_string | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Going Deeper With Convolutions | [
"Christian Szegedy",
"Wei Liu",
"Yangqing Jia",
"Pierre Sermanet",
"Scott Reed",
"Dragomir Anguelov",
"Dumitru Erhan",
"Vincent Vanhoucke",
"Andrew Rabinovich"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf | null | 1409.4842 | title_snapshot | @InProceedings{Szegedy_2015_CVPR,author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},title = {Going Deeper With Convolutions},booktitle = {Proceedings of the IEEE Conference on Computer ... | We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC2014). The main hallmark of this architecture is the improved utilization of the computing resource... | [
0.005320408381521702,
-0.04100751876831055,
0.010432800278067589,
0.039142947643995285,
0.011435859836637974,
0.04559820145368576,
0.01029970496892929,
-0.0035383240319788456,
0.019673477858304977,
-0.0456104576587677,
-0.0011399647919461131,
-0.004293307662010193,
-0.07020142674446106,
0.... |
1 | Propagated Image Filtering | [
"Jen-Hao Rick Chang",
"Yu-Chiang Frank Wang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Chang_Propagated_Image_Filtering_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Chang_Propagated_Image_Filtering_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Chang_Propagated_Image_Filtering_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Chang_2015_CVPR,author = {Rick Chang, Jen-Hao and Frank Wang, Yu-Chiang},title = {Propagated Image Filtering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions. In particular, our filter does not to utilize explicit spatial kernel functions as bilateral and guided filters do... | [
0.032183919101953506,
0.003561936551705003,
0.021547909826040268,
0.023433931171894073,
0.05391300469636917,
0.0479915551841259,
0.005791991483420134,
-0.033922433853149414,
-0.04939370974898338,
-0.08228316158056259,
-0.01137572806328535,
0.042447611689567566,
-0.07224705070257187,
0.0355... |
2 | Web Scale Photo Hash Clustering on A Single Machine | [
"Yunchao Gong",
"Marcin Pawlowski",
"Fei Yang",
"Louis Brandy",
"Lubomir Bourdev",
"Rob Fergus"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Gong_Web_Scale_Photo_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Gong_Web_Scale_Photo_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Gong_2015_CVPR,author = {Gong, Yunchao and Pawlowski, Marcin and Yang, Fei and Brandy, Louis and Bourdev, Lubomir and Fergus, Rob},title = {Web Scale Photo Hash Clustering on A Single Machine},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June... | This paper addresses the problem of clustering a very large number of photos (i.e. hundreds of millions a day) in a stream into millions of clusters. This is particularly important as the popularity of photo sharing websites, such as Facebook, Google, and Instagram. Given large number of photos available online, how to... | [
0.00017186808690894395,
-0.020725708454847336,
-0.010653045028448105,
0.041386913508176804,
0.04424024000763893,
0.023853328078985214,
0.04306231811642647,
0.0150726567953825,
-0.029172761365771294,
-0.018532713875174522,
-0.009141930378973484,
-0.05950987711548805,
-0.059409644454717636,
... |
3 | Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos | [
"Alina Kuznetsova",
"Sung Ju Hwang",
"Bodo Rosenhahn",
"Leonid Sigal"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Kuznetsova_Expanding_Object_Detectors_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Kuznetsova_Expanding_Object_Detectors_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Kuznetsova_Expanding_Object_Detectors_2015_CVPR_supplemental.zip | null | null | @InProceedings{Kuznetsova_2015_CVPR,author = {Kuznetsova, Alina and Ju Hwang, Sung and Rosenhahn, Bodo and Sigal, Leonid},title = {Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitio... | Over the last several years it has been shown that image-based object detectors are sensitive to the training data and often fail to generalize to examples that fall outside the original training sample domain (e.g., videos). A number of domain adaptation (DA) techniques have been proposed to address this problem. ... | [
0.014588944613933563,
-0.022452611476182938,
0.015767894685268402,
0.02963174507021904,
0.04629639908671379,
0.018234118819236755,
0.027439402416348457,
-0.013860974460840225,
-0.047585487365722656,
-0.03952334448695183,
-0.029437772929668427,
0.018806735053658485,
-0.07210175693035126,
-0... |
4 | Supervised Discrete Hashing | [
"Fumin Shen",
"Chunhua Shen",
"Wei Liu",
"Heng Tao Shen"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Shen_Supervised_Discrete_Hashing_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Shen_Supervised_Discrete_Hashing_2015_CVPR_paper.pdf | null | 1503.01557 | title_snapshot | @InProceedings{Shen_2015_CVPR,author = {Shen, Fumin and Shen, Chunhua and Liu, Wei and Tao Shen, Heng},title = {Supervised Discrete Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Recently, learning based hashing techniques have attracted broad research interests due to the resulting efficient storage and retrieval of images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the needed hash codes, which typically makes ha... | [
-0.00023130347835831344,
-0.032437823712825775,
-0.0270370040088892,
0.06981038302183151,
0.04400253668427467,
0.008312705904245377,
0.015455725602805614,
-0.00862257368862629,
-0.031053271144628525,
-0.02685263194143772,
-0.010890698991715908,
-0.03704214096069336,
-0.05883455276489258,
0... |
5 | What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions? | [
"Mihir Jain",
"Jan C. van Gemert",
"Cees G. M. Snoek"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Jain_What_do_15000_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Jain_What_do_15000_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Jain_2015_CVPR,author = {Jain, Mihir and van Gemert, Jan C. and Snoek, Cees G. M.},title = {What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper contributes to automatic classification and localization of human actions in video. Whereas motion is the key ingredient in modern approaches, we assess the benefits of having objects in the video representation. Rather than considering a handful of carefully selected and localized objects, we conduct an emp... | [
0.010032539255917072,
-0.013391807675361633,
0.001499439007602632,
0.039510540664196014,
0.016428107395768166,
0.002970632165670395,
0.028420258313417435,
0.015543700195848942,
-0.025720877572894096,
-0.024927334859967232,
-0.04778919741511345,
0.007422239985316992,
-0.07139353454113007,
-... |
6 | Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation | [
"Rahaf Aljundi",
"Remi Emonet",
"Damien Muselet",
"Marc Sebban"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Aljundi_Landmarks-Based_Kernelized_Subspace_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Aljundi_Landmarks-Based_Kernelized_Subspace_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Aljundi_2015_CVPR,author = {Aljundi, Rahaf and Emonet, Remi and Muselet, Damien and Sebban, Marc},title = {Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmark... | [
-0.01265192124992609,
0.0016840401804074645,
0.02737526036798954,
0.03323276340961456,
0.03623616695404053,
0.02318987436592579,
0.018271811306476593,
-0.011904816143214703,
0.006380700506269932,
-0.03280486911535263,
-0.03888221085071564,
0.014067019335925579,
-0.07639004290103912,
0.0047... |
7 | Blur Kernel Estimation Using Normalized Color-Line Prior | [
"Wei-Sheng Lai",
"Jian-Jiun Ding",
"Yen-Yu Lin",
"Yung-Yu Chuang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Lai_Blur_Kernel_Estimation_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Lai_Blur_Kernel_Estimation_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Lai_Blur_Kernel_Estimation_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Lai_2015_CVPR,author = {Lai, Wei-Sheng and Ding, Jian-Jiun and Lin, Yen-Yu and Chuang, Yung-Yu},title = {Blur Kernel Estimation Using Normalized Color-Line Prior},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper proposes a single-image blur kernel estimation algorithm that utilizes the normalized color-line prior to restore sharp edges without altering edge structures or enhancing noise. The proposed prior is derived from the color-line model, which has been successfully applied to non-blind deconvolution and many c... | [
0.012144545093178749,
-0.0020082304254174232,
0.015215182676911354,
0.06526997685432434,
0.05212414264678955,
0.049729350954294205,
0.022076470777392387,
0.016151949763298035,
-0.047424063086509705,
-0.05797860771417618,
-0.045218098908662796,
0.0292520672082901,
-0.03456952050328255,
-0.0... |
8 | A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors | [
"Nikhil Naik",
"Achuta Kadambi",
"Christoph Rhemann",
"Shahram Izadi",
"Ramesh Raskar",
"Sing Bing Kang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Naik_A_Light_Transport_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Naik_A_Light_Transport_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Naik_A_Light_Transport_2015_CVPR_supplemental.pdf | 1501.04878 | title_judge | @InProceedings{Naik_2015_CVPR,author = {Naik, Nikhil and Kadambi, Achuta and Rhemann, Christoph and Izadi, Shahram and Raskar, Ramesh and Bing Kang, Sing},title = {A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors},booktitle = {Proceedings of the IEEE Conference on Computer Vision ... | Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics. However, the depth images obtained from TOF cameras contain scene dependent errors due to multipath interference (MPI). Specifically, MPI occurs when multiple optical re... | [
0.015494200401008129,
0.016715867444872856,
-0.029819699004292488,
0.016763528808951378,
0.05533243715763092,
0.007712087128311396,
0.03230064734816551,
0.01700938306748867,
-0.02588500827550888,
-0.07466626167297363,
0.02427678368985653,
-0.004045956302434206,
-0.02883327379822731,
0.0369... |
9 | Traditional Saliency Reloaded: A Good Old Model in New Shape | [
"Simone Frintrop",
"Thomas Werner",
"German Martin Garcia"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Frintrop_2015_CVPR,author = {Frintrop, Simone and Werner, Thomas and Martin Garcia, German},title = {Traditional Saliency Reloaded: A Good Old Model in New Shape},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we show that the seminal, biologically-inspired saliency model by Itti et al. is still competitive with current state-of-the-art methods for salient object segmentation if some important adaptions are made. We show which changes are necessary to achieve high performance, with special emphasis on... | [
0.0021984572522342205,
0.0006309557938948274,
0.04768488556146622,
0.013523554429411888,
0.006575272884219885,
0.03651734068989754,
0.028432434424757957,
0.016352050006389618,
-0.05718820169568062,
-0.06140964850783348,
-0.03964579105377197,
0.010412434116005898,
-0.04711451008915901,
-0.0... |
10 | Automatic Construction Of Robust Spherical Harmonic Subspaces | [
"Patrick Snape",
"Yannis Panagakis",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Snape_Automatic_Construction_Of_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Snape_Automatic_Construction_Of_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Snape_Automatic_Construction_Of_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Snape_2015_CVPR,author = {Snape, Patrick and Panagakis, Yannis and Zafeiriou, Stefanos},title = {Automatic Construction Of Robust Spherical Harmonic Subspaces},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images. We combine existing techniques for uncalibrated photometric stereo and low rank matrix decompositions in order to robustly recover a combined model of shape and i... | [
-0.004135146737098694,
0.015451792627573013,
0.0291415024548769,
-0.007638748735189438,
0.0011285321088507771,
0.043714240193367004,
0.0317835696041584,
0.0008709582616575062,
-0.02900269627571106,
-0.055783361196517944,
-0.004957044031471014,
-0.019576380029320717,
-0.0899197980761528,
-0... |
11 | Leveraging Stereo Matching With Learning-Based Confidence Measures | [
"Min-Gyu Park",
"Kuk-Jin Yoon"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Park_Leveraging_Stereo_Matching_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Park_Leveraging_Stereo_Matching_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Park_Leveraging_Stereo_Matching_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Park_2015_CVPR,author = {Park, Min-Gyu and Yoon, Kuk-Jin},title = {Leveraging Stereo Matching With Learning-Based Confidence Measures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression forest framework to select effective confidence measures using training data. We then train regression fo... | [
0.03289829194545746,
-0.001816977164708078,
0.010442337952554226,
0.054287876933813095,
0.04540783539414406,
0.06385285407304764,
0.0444326214492321,
-0.007487204857170582,
0.007521709427237511,
-0.05070767551660538,
-0.03668636456131935,
0.0217660553753376,
-0.0766722559928894,
-0.0077560... |
12 | Saliency Detection via Cellular Automata | [
"Yao Qin",
"Huchuan Lu",
"Yiqun Xu",
"He Wang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Qin_Saliency_Detection_via_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Qin_Saliency_Detection_via_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Qin_Saliency_Detection_via_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Qin_2015_CVPR,author = {Qin, Yao and Lu, Huchuan and Xu, Yiqun and Wang, He},title = {Saliency Detection via Cellular Automata},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we introduce Cellular Automata--a dynamic evolution model to intuitively detect the salient object. First, we construct a background-based map using color and space contrast with the clustered boundary seeds. Then, a novel propagation mechanism dependent on Cellular Automata is proposed to exploit the in... | [
-0.004445834085345268,
0.024940920993685722,
0.014098639599978924,
0.007306796498596668,
0.04577666521072388,
0.04426482692360878,
0.01829611137509346,
0.02971620112657547,
-0.04264272376894951,
-0.06370722502470016,
-0.013054790906608105,
-0.00738571397960186,
-0.06290272623300552,
-0.006... |
13 | Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers | [
"Jonas Wulff",
"Michael J. Black"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Wulff_Efficient_Sparse-to-Dense_Optical_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Wulff_Efficient_Sparse-to-Dense_Optical_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Wulff_2015_CVPR,author = {Wulff, Jonas and Black, Michael J.},title = {Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We address the elusive goal of estimating optical flow both accurately and efficiently by adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields. We learn the principal components of natural flow fields using flow computed... | [
0.021446406841278076,
-0.01946849189698696,
0.038045067340135574,
0.039154328405857086,
0.025235196575522423,
0.031259458512067795,
0.01459386758506298,
0.011623330414295197,
-0.03343302756547928,
-0.06235211342573166,
0.006125072482973337,
-0.03899538889527321,
-0.06068255379796028,
0.000... |
14 | Learning Multiple Visual Tasks While Discovering Their Structure | [
"Carlo Ciliberto",
"Lorenzo Rosasco",
"Silvia Villa"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Ciliberto_Learning_Multiple_Visual_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Ciliberto_Learning_Multiple_Visual_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Ciliberto_Learning_Multiple_Visual_2015_CVPR_supplemental.pdf | 1504.03106 | title_snapshot | @InProceedings{Ciliberto_2015_CVPR,author = {Ciliberto, Carlo and Rosasco, Lorenzo and Villa, Silvia},title = {Learning Multiple Visual Tasks While Discovering Their Structure},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Multi-task learning is a natural approach for computer vision applications that require the simultaneous solution of several distinct but related problems, e.g. object detection, classification, tracking of multiple agents, or denoising, to name a few. The key idea is that exploring task relatedness (structure) can lea... | [
0.028414899483323097,
-0.009039751254022121,
0.026949552819132805,
0.026272399351000786,
0.015437237918376923,
0.05826385319232941,
0.006241069175302982,
0.0005298033938743174,
-0.033359695225954056,
-0.07299645990133286,
-0.026885509490966797,
0.022675955668091774,
-0.07348044961690903,
-... |
15 | Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition | [
"Zhiwu Huang",
"Ruiping Wang",
"Shiguang Shan",
"Xilin Chen"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Projection_Metric_Learning_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Huang_Projection_Metric_Learning_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Huang_2015_CVPR,author = {Huang, Zhiwu and Wang, Ruiping and Shan, Shiguang and Chen, Xilin},title = {Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month... | In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Gr... | [
-0.018676748499274254,
-0.01648063026368618,
0.029437363147735596,
0.02903275191783905,
0.0095086470246315,
0.04975664243102074,
0.02829747088253498,
-0.02330571599304676,
-0.012144943699240685,
-0.060565367341041565,
-0.02511466294527054,
-0.014595606364309788,
-0.07942165434360504,
0.022... |
16 | Structural Sparse Tracking | [
"Tianzhu Zhang",
"Si Liu",
"Changsheng Xu",
"Shuicheng Yan",
"Bernard Ghanem",
"Narendra Ahuja",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Structural_Sparse_Tracking_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Structural_Sparse_Tracking_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Zhang_2015_CVPR,author = {Zhang, Tianzhu and Liu, Si and Xu, Changsheng and Yan, Shuicheng and Ghanem, Bernard and Ahuja, Narendra and Yang, Ming-Hsuan},title = {Structural Sparse Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and in... | [
0.015796860679984093,
0.0102183623239398,
0.02529846876859665,
0.05029865726828575,
0.04584527388215065,
0.029001524671912193,
0.0025144563987851143,
0.02004789561033249,
-0.07778353989124298,
-0.06837280839681625,
0.01601491868495941,
-0.013689015060663223,
-0.06416011601686478,
-0.029560... |
17 | Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation | [
"HyeokHyen Kwon",
"Yu-Wing Tai",
"Stephen Lin"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Kwon_Data-Driven_Depth_Map_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Kwon_Data-Driven_Depth_Map_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Kwon_Data-Driven_Depth_Map_2015_CVPR_supplemental.zip | null | null | @InProceedings{Kwon_2015_CVPR,author = {Kwon, HyeokHyen and Tai, Yu-Wing and Lin, Stephen},title = {Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Depth maps captured by consumer-level depth cameras such as Kinect are usually degraded by noise, missing values, and quantization. In this paper, we present a data-driven approach for refining degraded RAW depth maps that are coupled with an RGB image. The key idea of our approach is to take advantage of a training se... | [
-0.007211612071841955,
0.0009799414547160268,
-0.0014832046581432223,
0.041442371904850006,
0.03033069707453251,
0.07266876846551895,
0.025691505521535873,
0.018775304779410362,
-0.026008211076259613,
-0.06344597786664963,
0.024738922715187073,
-0.010990011505782604,
-0.048220209777355194,
... |
18 | Uncalibrated Photometric Stereo Based on Elevation Angle Recovery From BRDF Symmetry of Isotropic Materials | [
"Feng Lu",
"Imari Sato",
"Yoichi Sato"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Lu_Uncalibrated_Photometric_Stereo_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Lu_Uncalibrated_Photometric_Stereo_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Lu_2015_CVPR,author = {Lu, Feng and Sato, Imari and Sato, Yoichi},title = {Uncalibrated Photometric Stereo Based on Elevation Angle Recovery From BRDF Symmetry of Isotropic Materials},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | This paper addresses the problem of uncalibrated photometric stereo with isotropic reflectances. Existing methods face difficulty in solving for the elevation angles of surface normals when the light sources only cover the visible hemisphere. Here, we introduce the notion of "constrained half-vector symmetry" for gener... | [
0.01089145801961422,
0.02348410151898861,
0.03725242242217064,
-0.014394333586096764,
0.031265996396541595,
0.008680744096636772,
0.020702190697193146,
0.009448510594666004,
-0.03414573892951012,
-0.06939245015382767,
-0.01909537985920906,
-0.01305750198662281,
-0.0408455953001976,
0.01271... |
19 | Attributes and Categories for Generic Instance Search From One Example | [
"Ran Tao",
"Arnold W.M. Smeulders",
"Shih-Fu Chang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Tao_Attributes_and_Categories_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Tao_Attributes_and_Categories_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Tao_2015_CVPR,author = {Tao, Ran and Smeulders, Arnold W.M. and Chang, Shih-Fu},title = {Attributes and Categories for Generic Instance Search From One Example},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos. Firstly, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses i... | [
0.0026615140959620476,
-0.006938309408724308,
0.025081835687160492,
0.03926478698849678,
0.028713472187519073,
0.03762141987681389,
0.039012882858514786,
0.005033636931329966,
-0.02846130169928074,
-0.031972646713256836,
-0.06808091700077057,
0.0035322816111147404,
-0.08530910313129425,
0.... |
20 | Heat Diffusion Over Weighted Manifolds: A New Descriptor for Textured 3D Non-Rigid Shapes | [
"Mostafa Abdelrahman",
"Aly Farag",
"David Swanson",
"Moumen T. El-Melegy"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Abdelrahman_Heat_Diffusion_Over_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Abdelrahman_Heat_Diffusion_Over_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Abdelrahman_Heat_Diffusion_Over_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Abdelrahman_2015_CVPR,author = {Abdelrahman, Mostafa and Farag, Aly and Swanson, David and El-Melegy, Moumen T.},title = {Heat Diffusion Over Weighted Manifolds: A New Descriptor for Textured 3D Non-Rigid Shapes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (... | This paper propose an approach for modeling textured 3D non-rigid models based on Weighted Heat Kernel Signature(W-HKS). As a first contribution, we show how to include photometric information as a weight over the shape manifold, we also propose a novel formulation for heat diffusion over weighted manifolds. As a secon... | [
-0.011621758341789246,
-0.007218799088150263,
0.04526229575276375,
0.03465262055397034,
0.027864310890436172,
0.06273598223924637,
-0.011832713149487972,
-0.030467288568615913,
-0.0208180733025074,
-0.0805860385298729,
-0.02805989794433117,
-0.017289765179157257,
-0.022124337032437325,
0.0... |
21 | A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images | [
"Christopher Zach",
"Adrian Penate-Sanchez",
"Minh-Tri Pham"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zach_A_Dynamic_Programming_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zach_A_Dynamic_Programming_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Zach_A_Dynamic_Programming_2015_CVPR_supplemental.zip | null | null | @InProceedings{Zach_2015_CVPR,author = {Zach, Christopher and Penate-Sanchez, Adrian and Pham, Minh-Tri},title = {A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June... | Joint object recognition and pose estimation solely from range images is an important task e.g. in robotics applications and in automated manufacturing environments. The lack of color information and limitations of current commodity depth sensors make this task a challenging computer vision problem, and a standard rand... | [
0.024124743416905403,
0.013283793814480305,
-0.038879286497831345,
0.040331725031137466,
0.04380675405263901,
0.05877378210425377,
0.013461275026202202,
0.0028424703050404787,
-0.028494171798229218,
-0.04386231303215027,
-0.006253233645111322,
0.01209716871380806,
-0.07838694751262665,
-0.... |
22 | Beyond Gaussian Pyramid: Multi-Skip Feature Stacking for Action Recognition | [
"Zhengzhong Lan",
"Ming Lin",
"Xuanchong Li",
"Alex G. Hauptmann",
"Bhiksha Raj"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Lan_Beyond_Gaussian_Pyramid_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Lan_Beyond_Gaussian_Pyramid_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Lan_Beyond_Gaussian_Pyramid_2015_CVPR_supplemental.pdf | 1411.6660 | title_snapshot | @InProceedings{Lan_2015_CVPR,author = {Lan, Zhengzhong and Lin, Ming and Li, Xuanchong and Hauptmann, Alex G. and Raj, Bhiksha},title = {Beyond Gaussian Pyramid: Multi-Skip Feature Stacking for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuation introduces bias to the resulting features and generates ill-conditioned feature matrices. The Gaussian Pyramid has been used as a feature... | [
0.008750137872993946,
-0.019718240946531296,
-0.008058110252022743,
0.03624551370739937,
0.04269181191921234,
0.032715991139411926,
0.043070875108242035,
-0.014567223377525806,
-0.03716135397553444,
-0.05633411183953285,
0.023964958265423775,
-0.0026100273244082928,
-0.07141700387001038,
-... |
23 | A Geodesic-Preserving Method for Image Warping | [
"Dongping Li",
"Kaiming He",
"Jian Sun",
"Kun Zhou"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Li_A_Geodesic-Preserving_Method_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Li_A_Geodesic-Preserving_Method_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Li_A_Geodesic-Preserving_Method_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Li_2015_CVPR,author = {Li, Dongping and He, Kaiming and Sun, Jian and Zhou, Kun},title = {A Geodesic-Preserving Method for Image Warping},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | The manipulation of panoramic/wide-angle images is usually achieved via image warping. Though various techniques have been developed for preserving shapes and straight lines for warping, these are not sufficient for panoramic/wide-angle images. The image projections will turn the straight lines into curved "geodesic li... | [
0.014148597605526447,
-0.0003081268514506519,
0.017619937658309937,
0.02024024911224842,
0.04204704985022545,
0.04246116429567337,
0.02511090226471424,
0.04143349826335907,
-0.05647232383489609,
-0.09370140731334686,
-0.014407889917492867,
-0.016238844022154808,
-0.06566230207681656,
0.021... |
24 | Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition | [
"Shaoxin Li",
"Junliang Xing",
"Zhiheng Niu",
"Shiguang Shan",
"Shuicheng Yan"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Li_Shape_Driven_Kernel_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Shape_Driven_Kernel_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2015_CVPR,author = {Li, Shaoxin and Xing, Junliang and Niu, Zhiheng and Shan, Shiguang and Yan, Shuicheng},title = {Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recogn... | One key challenge of facial traits recognition is the large non-rigid appearance variations due to irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neura... | [
-0.019383858889341354,
-0.023154517635703087,
0.03469698503613472,
0.021749449893832207,
0.026953700929880142,
0.08075680583715439,
0.030607596039772034,
0.003547952976077795,
0.001333827618509531,
-0.047878701239824295,
-0.0219956673681736,
0.02460015006363392,
-0.06475517153739929,
0.016... |
25 | From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement | [
"Marko Ristin",
"Juergen Gall",
"Matthieu Guillaumin",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Ristin_From_Categories_to_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Ristin_From_Categories_to_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Ristin_2015_CVPR,author = {Ristin, Marko and Gall, Juergen and Guillaumin, Matthieu and Van Gool, Luc},title = {From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitio... | The number of digital images is growing extremely rapidly, and so is the need for their classification. But, as more images of pre-defined categories become available, they also become more diverse and cover finer semantic differences. Ultimately, the categories themselves need to be divided into subcategories to acc... | [
-0.007942993193864822,
-0.04940233752131462,
-0.00857646856456995,
0.0333111546933651,
0.027198564261198044,
0.012063506990671158,
0.015287959948182106,
-0.02776039019227028,
-0.03887001797556877,
-0.013476021587848663,
-0.03156689181923866,
-0.005963786039501429,
-0.06742069870233536,
0.0... |
26 | Combination Features and Models for Human Detection | [
"Yunsheng Jiang",
"Jinwen Ma"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Jiang_Combination_Features_and_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Jiang_Combination_Features_and_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Jiang_2015_CVPR,author = {Jiang, Yunsheng and Ma, Jinwen},title = {Combination Features and Models for Human Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper presents effective combination models with certain combination features for human detection. In the past several years, many existing features/models have achieved impressive progress, but their performances are still limited by the biases rooted in their self-structures, that is, a particular kind of featur... | [
-0.005767853930592537,
-0.012386997230350971,
0.003525444306433201,
0.024129895493388176,
0.042800500988960266,
0.037742432206869125,
0.006211469415575266,
0.018154287710785866,
-0.06015847995877266,
-0.06345518678426743,
-0.02722373604774475,
-0.007777675054967403,
-0.0706782415509224,
-0... |
27 | Improving Object Detection With Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [
"Yuting Zhang",
"Kihyuk Sohn",
"Ruben Villegas",
"Gang Pan",
"Honglak Lee"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Improving_Object_Detection_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Improving_Object_Detection_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Zhang_Improving_Object_Detection_2015_CVPR_supplemental.pdf | 1504.03293 | title_snapshot | @InProceedings{Zhang_2015_CVPR,author = {Zhang, Yuting and Sohn, Kihyuk and Villegas, Ruben and Pan, Gang and Lee, Honglak},title = {Improving Object Detection With Deep Convolutional Networks via Bayesian Optimization and Structured Prediction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pat... | Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of e... | [
0.01943063922226429,
-0.0011251524556428194,
0.01704329065978527,
0.04618498310446739,
0.028343509882688522,
0.03669361770153046,
0.0010113305179402232,
-0.0021935845725238323,
-0.011279567144811153,
-0.05161423981189728,
-0.03016696870326996,
0.006934979930520058,
-0.03720424696803093,
-0... |
28 | A Metric Parametrization for Trifocal Tensors With Non-Colinear Pinholes | [
"Spyridon Leonardos",
"Roberto Tron",
"Kostas Daniilidis"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Leonardos_A_Metric_Parametrization_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Leonardos_A_Metric_Parametrization_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Leonardos_2015_CVPR,author = {Leonardos, Spyridon and Tron, Roberto and Daniilidis, Kostas},title = {A Metric Parametrization for Trifocal Tensors With Non-Colinear Pinholes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | The trifocal tensor, which describes the relation between projections of points and lines in three views, is a fundamental entity of geometric computer vision. In this work, we investigate a new parametrization of the trifocal tensor for calibrated cameras with non-colinear pinholes obtained from a quotient Riemannian ... | [
-0.01907174289226532,
0.027651894837617874,
0.008194115944206715,
-0.0038876638282090425,
0.024417297914624214,
0.05650648474693298,
-0.01404194813221693,
0.014634110033512115,
-0.05878386273980141,
-0.05958104133605957,
-0.02365942858159542,
0.002436410402879119,
-0.07315412908792496,
0.0... |
29 | An Efficient Volumetric Framework for Shape Tracking | [
"Benjamin Allain",
"Jean-Sebastien Franco",
"Edmond Boyer"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Allain_An_Efficient_Volumetric_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Allain_An_Efficient_Volumetric_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Allain_An_Efficient_Volumetric_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Allain_2015_CVPR,author = {Allain, Benjamin and Franco, Jean-Sebastien and Boyer, Edmond},title = {An Efficient Volumetric Framework for Shape Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Recovering 3D shape motion using visual information is an important problem with many applications in computer vision and computer graphics, among other domains. Most existing approaches rely on surface-based strategies, where surface models are fit to visual surface observations. While numerically plausible, this par... | [
0.019130250439047813,
0.01657341606914997,
0.014601708389818668,
0.011784025467932224,
0.004561321344226599,
0.05929180607199669,
-0.014148322865366936,
0.03983055427670479,
-0.06082342192530632,
-0.09740516543388367,
-0.02477211132645607,
-0.02358166128396988,
-0.03135161101818085,
-0.001... |
30 | Structured Sparse Subspace Clustering: A Unified Optimization Framework | [
"Chun-Guang Li",
"Rene Vidal"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Li_Structured_Sparse_Subspace_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Structured_Sparse_Subspace_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2015_CVPR,author = {Li, Chun-Guang and Vidal, Rene},title = {Structured Sparse Subspace Clustering: A Unified Optimization Framework},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State of the art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segment... | [
0.01408980879932642,
-0.014195979572832584,
0.03480462357401848,
0.023082803934812546,
0.04377346113324165,
0.03513769805431366,
0.01096748374402523,
-0.001127838622778654,
-0.04369577020406723,
-0.04410185664892197,
-0.01556429173797369,
-0.020404329523444176,
-0.07312106341123581,
-0.009... |
31 | Delving Into Egocentric Actions | [
"Yin Li",
"Zhefan Ye",
"James M. Rehg"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Li_Delving_Into_Egocentric_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Delving_Into_Egocentric_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2015_CVPR,author = {Li, Yin and Ye, Zhefan and Rehg, James M.},title = {Delving Into Egocentric Actions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We address the challenging problem of recognizing the camera wearer's actions from videos captured by an egocentric camera. Egocentric videos encode a rich set of signals regarding the camera wearer, including head movement, hand pose and gaze information. We propose to utilize these mid-level egocentric cues for egoce... | [
0.033441800624132156,
-0.007896779105067253,
-0.0015123431803658605,
0.011870473623275757,
0.019742943346500397,
0.012195192277431488,
0.03369666263461113,
-0.0036942237056791782,
-0.006595880724489689,
-0.014325364492833614,
-0.028142115101218224,
0.0011839017970487475,
-0.05856272578239441... |
32 | Latent Trees for Estimating Intensity of Facial Action Units | [
"Sebastian Kaltwang",
"Sinisa Todorovic",
"Maja Pantic"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Kaltwang_Latent_Trees_for_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Kaltwang_Latent_Trees_for_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Kaltwang_Latent_Trees_for_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Kaltwang_2015_CVPR,author = {Kaltwang, Sebastian and Todorovic, Sinisa and Pantic, Maja},title = {Latent Trees for Estimating Intensity of Facial Action Units},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper is about estimating intensity levels of Facial Action Units (FAUs) in videos as an important and challenging step toward interpreting facial expressions. To address uncertainty in detections of facial landmark points, used as out input features, we formulate a new generative framework comprised of a graphica... | [
0.0264018215239048,
0.0023938415106385946,
-0.00507260812446475,
0.013137008994817734,
-0.004658693913370371,
0.06200136989355087,
0.06596040725708008,
0.025172406807541847,
-0.016102373600006104,
-0.01999155804514885,
-0.011160299181938171,
-0.013377870433032513,
-0.04956169053912163,
-0.... |
33 | Robust Regression on Image Manifolds for Ordered Label Denoising | [
"Hui Wu",
"Richard Souvenir"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Wu_Robust_Regression_on_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Wu_Robust_Regression_on_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Wu_Robust_Regression_on_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Wu_2015_CVPR,author = {Wu, Hui and Souvenir, Richard},title = {Robust Regression on Image Manifolds for Ordered Label Denoising},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we present a computationally efficient and non-parametric method for robust regression on manifolds. We apply our algorithm to the problem of correcting mislabeled examples from image collections with ordered (e.g., real-valued, ordinal) labels. Compared to related methods for robust regression, our meth... | [
-0.004978610202670097,
-0.013043368235230446,
-0.0018198146717622876,
0.04138481244444847,
0.019228272140026093,
0.057052161544561386,
0.026504037901759148,
-0.018461966887116432,
-0.03719330579042435,
-0.058489829301834106,
-0.025343820452690125,
0.023015135899186134,
-0.07919971644878387,
... |
34 | Privacy Preserving Optics for Miniature Vision Sensors | [
"Francesco Pittaluga",
"Sanjeev J. Koppal"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Pittaluga_Privacy_Preserving_Optics_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Pittaluga_Privacy_Preserving_Optics_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Pittaluga_Privacy_Preserving_Optics_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Pittaluga_2015_CVPR,author = {Pittaluga, Francesco and Koppal, Sanjeev J.},title = {Privacy Preserving Optics for Miniature Vision Sensors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | The next wave of micro and nano devices will create a world with trillions of small networked cameras. This will lead to increased concerns about privacy and security. Most privacy preserving algorithms for computer vision are applied after image/video data has been captured. We propose to use privacy preserving optics... | [
0.014766371808946133,
0.02485097199678421,
0.018053030595183372,
0.038165293633937836,
0.052383750677108765,
0.020629746839404106,
0.011978103779256344,
-0.0060440544039011,
-0.03851912170648575,
-0.02529996633529663,
-0.0058418214321136475,
-0.0009542303159832954,
-0.051666319370269775,
0... |
35 | Deep Transfer Metric Learning | [
"Junlin Hu",
"Jiwen Lu",
"Yap-Peng Tan"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Hu_Deep_Transfer_Metric_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Hu_Deep_Transfer_Metric_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Hu_2015_CVPR,author = {Hu, Junlin and Lu, Jiwen and Tan, Yap-Peng},title = {Deep Transfer Metric Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In t... | [
-0.017667673528194427,
-0.006727165076881647,
0.0082596056163311,
0.019046084955334663,
0.03984827548265457,
0.03241485729813576,
0.03156545013189316,
0.0037340414710342884,
-0.00392018212005496,
-0.03572436422109604,
-0.024878209456801414,
-0.0070519526489079,
-0.05831114947795868,
-0.006... |
36 | Small-Variance Nonparametric Clustering on the Hypersphere | [
"Julian Straub",
"Trevor Campbell",
"Jonathan P. How",
"John W. Fisher III"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Straub_Small-Variance_Nonparametric_Clustering_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Straub_Small-Variance_Nonparametric_Clustering_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Straub_Small-Variance_Nonparametric_Clustering_2015_CVPR_supplemental.zip | 1607.06407 | title_snapshot | @InProceedings{Straub_2015_CVPR,author = {Straub, Julian and Campbell, Trevor and How, Jonathan P. and Fisher, III, John W.},title = {Small-Variance Nonparametric Clustering on the Hypersphere},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015... | Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer vision applications, such as scene understanding, plane segmentation, and regularization of 3D reconstructions. Based on the small-variance l... | [
0.019455520436167717,
0.014776400290429592,
0.030945830047130585,
0.032493818551301956,
0.016754180192947388,
0.02841120772063732,
0.03204541280865669,
0.012769436463713646,
-0.017032288014888763,
-0.06418924033641815,
-0.002515175146982074,
-0.016766944900155067,
-0.058618661016225815,
0.... |
37 | DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time | [
"Richard A. Newcombe",
"Dieter Fox",
"Steven M. Seitz"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Newcombe_DynamicFusion_Reconstruction_and_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Newcombe_DynamicFusion_Reconstruction_and_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Newcombe_2015_CVPR,author = {Newcombe, Richard A. and Fox, Dieter and Seitz, Steven M.},title = {DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated g... | [
0.006371206603944302,
-0.023345816880464554,
0.006040805485099554,
0.02871580235660076,
0.03681131452322006,
0.06406449526548386,
0.01958397962152958,
0.045614439994096756,
-0.04901806637644768,
-0.07258277386426926,
-0.016775641590356827,
-0.03786259889602661,
-0.05341116338968277,
-0.017... |
38 | Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches | [
"Yang Li",
"Jianke Zhu",
"Steven C.H. Hoi"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Li_Reliable_Patch_Trackers_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Reliable_Patch_Trackers_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2015_CVPR,author = {Li, Yang and Zhu, Jianke and Hoi, Steven C.H.},title = {Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that ... | [
0.026500098407268524,
-0.026296650990843773,
0.0073217349126935005,
0.029177691787481308,
0.03454156219959259,
0.04071938991546631,
-0.003890672232955694,
0.018114686012268066,
-0.045204926282167435,
-0.07947903126478195,
-0.04002440720796585,
-0.006513996049761772,
-0.07016221433877945,
-... |
39 | Predicting Eye Fixations Using Convolutional Neural Networks | [
"Nian Liu",
"Junwei Han",
"Dingwen Zhang",
"Shifeng Wen",
"Tianming Liu"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2015_CVPR,author = {Liu, Nian and Han, Junwei and Zhang, Dingwen and Wen, Shifeng and Liu, Tianming},title = {Predicting Eye Fixations Using Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors. In this paper, we propose a novel computational framework to simultaneously learn these two types of visual features from raw image data using a multiresolution convolutional n... | [
-0.0028952143620699644,
0.04760759696364403,
0.025229526683688164,
0.012704620137810707,
0.04720252752304077,
0.019827798008918762,
0.02741038054227829,
0.024909501895308495,
-0.019184930250048637,
-0.016941316425800323,
-0.001477289479225874,
0.019843963906168938,
-0.0929357260465622,
-0.... |
40 | Kernel Fusion for Better Image Deblurring | [
"Long Mai",
"Feng Liu"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Mai_Kernel_Fusion_for_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Mai_Kernel_Fusion_for_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Mai_2015_CVPR,author = {Mai, Long and Liu, Feng},title = {Kernel Fusion for Better Image Deblurring},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Kernel estimation for image deblurring is a challenging task and a large number of algorithms have been developed. Our hypothesis is that while individual kernels estimated using different methods alone are sometimes inadequate, they often complement each other. This paper addresses the problem of fusing multiple kerne... | [
-0.017295440658926964,
-0.016737760975956917,
0.03451055288314819,
0.08956864476203918,
0.03254522755742073,
0.014413752593100071,
-0.0105978948995471,
0.005455110687762499,
-0.04753775894641876,
-0.05478956550359726,
-0.05176405608654022,
0.014247778803110123,
-0.039499275386333466,
0.001... |
41 | Direction Matters: Depth Estimation With a Surface Normal Classifier | [
"Christian Hane",
"Lubor Ladicky",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Hane_Direction_Matters_Depth_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Hane_Direction_Matters_Depth_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Hane_2015_CVPR,author = {Hane, Christian and Ladicky, Lubor and Pollefeys, Marc},title = {Direction Matters: Depth Estimation With a Surface Normal Classifier},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this work we make use of recent advances in data driven classification to improve standard approaches for binocular stereo matching and single view depth estimation. Surface normal direction estimation has become feasible and shown to work reliably on state of the art benchmark datasets. Information about the surfac... | [
0.019700227305293083,
0.03956219181418419,
0.02539564110338688,
0.012769072316586971,
0.024432264268398285,
0.046348754316568375,
0.03847531974315643,
0.01549825444817543,
-0.017921682447195053,
-0.07156075537204742,
-0.017433956265449524,
0.009066423401236534,
-0.0781673714518547,
0.01107... |
42 | Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection | [
"George Papandreou",
"Iasonas Kokkinos",
"Pierre-Andre Savalle"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Papandreou_Modeling_Local_and_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Papandreou_Modeling_Local_and_2015_CVPR_paper.pdf | null | 1412.0296 | title_judge | @InProceedings{Papandreou_2015_CVPR,author = {Papandreou, George and Kokkinos, Iasonas and Savalle, Pierre-Andre},title = {Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection},booktitle = {Proceedings of the IEEE Conference on Computer ... | Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple 'max-pooling' (MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and efficiency of DCNNs. First, we introduce epitomic convolution as an alte... | [
-0.00777812534943223,
0.006758544128388166,
-0.007967785932123661,
0.04014354944229126,
0.022620635107159615,
0.052930574864149094,
-0.0017399118514731526,
0.028917187824845314,
-0.02276884764432907,
-0.04533759877085686,
-0.031112974509596825,
-0.010187702253460884,
-0.03814035281538963,
... |
43 | Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision | [
"Yezhou Yang",
"Cornelia Fermuller",
"Yi Li",
"Yiannis Aloimonos"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Yang_Grasp_Type_Revisited_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Yang_Grasp_Type_Revisited_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Yang_2015_CVPR,author = {Yang, Yezhou and Fermuller, Cornelia and Li, Yi and Aloimonos, Yiannis},title = {Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | The grasp type provides crucial information about human action. However, recognizing the grasp type in unconstrained scenes is challenging because of the large variations in appearance, occlusions and geometric distortions. In this paper, first we present a convolutional neural network to classify functional hand gras... | [
-0.007897487841546535,
-0.007600302807986736,
-0.02398565597832203,
-0.0025143076200038195,
0.027766510844230652,
0.03870704025030136,
0.022484801709651947,
0.019798366352915764,
-0.04330240935087204,
-0.04047708585858345,
-0.0219794362783432,
-0.005143297836184502,
-0.08666656911373138,
-... |
44 | Learning Hypergraph-Regularized Attribute Predictors | [
"Sheng Huang",
"Mohamed Elhoseiny",
"Ahmed Elgammal",
"Dan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Learning_Hypergraph-Regularized_Attribute_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Huang_Learning_Hypergraph-Regularized_Attribute_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Huang_Learning_Hypergraph-Regularized_Attribute_2015_CVPR_supplemental.pdf | 1503.05782 | title_snapshot | @InProceedings{Huang_2015_CVPR,author = {Huang, Sheng and Elhoseiny, Mohamed and Elgammal, Ahmed and Yang, Dan},title = {Learning Hypergraph-Regularized Attribute Predictors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jo... | [
0.03429349139332771,
-0.009595362469553947,
0.02965576946735382,
0.04631492495536804,
0.02214425802230835,
0.030010828748345375,
0.020765211433172226,
-0.0449361577630043,
0.005336214788258076,
-0.042740605771541595,
-0.0220587570220232,
0.035843223333358765,
-0.0762898251414299,
0.0055454... |
45 | A Coarse-to-Fine Model for 3D Pose Estimation and Sub-Category Recognition | [
"Roozbeh Mottaghi",
"Yu Xiang",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Mottaghi_A_Coarse-to-Fine_Model_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Mottaghi_A_Coarse-to-Fine_Model_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Mottaghi_A_Coarse-to-Fine_Model_2015_CVPR_supplemental.pdf | 1504.02764 | title_snapshot | @InProceedings{Mottaghi_2015_CVPR,author = {Mottaghi, Roozbeh and Xiang, Yu and Savarese, Silvio},title = {A Coarse-to-Fine Model for 3D Pose Estimation and Sub-Category Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Despite the fact that object detection, 3D pose estimation, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters. To jointly model all of these tasks, we propose a coarse-to-fine hierarchical representation, where each... | [
-0.010219131596386433,
0.006650625262409449,
0.009223395958542824,
0.02244351990520954,
0.030243290588259697,
0.03723955526947975,
0.01779932714998722,
0.002117330674082041,
-0.04685722663998604,
-0.027852803468704224,
-0.014095595106482506,
-0.001411904115229845,
-0.06981305032968521,
-0.... |
46 | Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images | [
"Anh Nguyen",
"Jason Yosinski",
"Jeff Clune"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Nguyen_Deep_Neural_Networks_2015_CVPR_supplemental.pdf | 1412.1897 | title_snapshot | @InProceedings{Nguyen_2015_CVPR,author = {Nguyen, Anh and Yosinski, Jason and Clune, Jeff},title = {Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {201... | Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences rem... | [
0.003586709965020418,
0.0037207533605396748,
-0.018257535994052887,
0.05690942332148552,
0.04334237799048424,
0.007025828119367361,
0.03241904079914093,
0.025028878822922707,
-0.014316420070827007,
-0.04694964736700058,
-0.032385461032390594,
0.005490307696163654,
-0.05318966507911682,
-0.... |
47 | Deformable Part Models are Convolutional Neural Networks | [
"Ross Girshick",
"Forrest Iandola",
"Trevor Darrell",
"Jitendra Malik"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Girshick_Deformable_Part_Models_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Girshick_Deformable_Part_Models_2015_CVPR_paper.pdf | null | 1409.5403 | title_snapshot | @InProceedings{Girshick_2015_CVPR,author = {Girshick, Ross and Iandola, Forrest and Darrell, Trevor and Malik, Jitendra},title = {Deformable Part Models are Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM can be formu... | [
0.0040358868427574635,
-0.0009889911161735654,
-0.03149854391813278,
0.036521848291158676,
0.0521915964782238,
0.06320326030254364,
0.002277027815580368,
0.030462278053164482,
-0.030382202938199043,
-0.05887537822127342,
-0.02532169222831726,
-0.021947741508483887,
-0.043187592178583145,
0... |
48 | Hypercolumns for Object Segmentation and Fine-Grained Localization | [
"Bharath Hariharan",
"Pablo Arbelaez",
"Ross Girshick",
"Jitendra Malik"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf | null | 1411.5752 | title_snapshot | @InProceedings{Hariharan_2015_CVPR,author = {Hariharan, Bharath and Arbelaez, Pablo and Girshick, Ross and Malik, Jitendra},title = {Hypercolumns for Object Segmentation and Fine-Grained Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation. However, the information in this layer may be too coarse to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To ge... | [
0.002166676102206111,
-0.00021625864610541612,
0.03606964275240898,
0.03063386306166649,
0.03949613496661186,
0.006195408292114735,
0.021383369341492653,
0.014295426197350025,
-0.03625619783997536,
-0.03607460856437683,
-0.017687181010842323,
-0.055571913719177246,
-0.049391284584999084,
0... |
49 | Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging | [
"Johannes Hofmanninger",
"Georg Langs"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Hofmanninger_Mapping_Visual_Features_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Hofmanninger_Mapping_Visual_Features_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Hofmanninger_2015_CVPR,author = {Hofmanninger, Johannes and Langs, Georg},title = {Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Content based image retrieval is highly relevant in medical imaging, since it makes vast amounts of imaging data accessible for comparison during diagnosis. Finding image similarity measures that reflect diagnostically relevant relationships is challenging, since the overall appearance variability is high compared to o... | [
0.00037184241227805614,
-0.011532236821949482,
0.003989216405898333,
0.027287505567073822,
0.05615346133708954,
-0.0029148359317332506,
0.007711512036621571,
0.003935470245778561,
-0.010688391514122486,
-0.03388098627328873,
-0.04570026323199272,
0.011211108416318893,
-0.03638208284974098,
... |
50 | Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision | [
"Stephan Schraml",
"Ahmed Nabil Belbachir",
"Horst Bischof"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Schraml_Event-Driven_Stereo_Matching_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Schraml_Event-Driven_Stereo_Matching_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Schraml_2015_CVPR,author = {Schraml, Stephan and Nabil Belbachir, Ahmed and Bischof, Horst},title = {Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper presents a stereo matching approach for a novel multi-perspective panoramic stereo vision system, making use of asynchronous and non-simultaneous stereo imaging towards real-time 3D 360deg vision. The method is designed for events representing the scenes visual contrast as a sparse visual code allowing the s... | [
0.03211662545800209,
0.04242965951561928,
-0.024395635351538658,
0.013263475149869919,
0.035319603979587555,
0.0729462131857872,
0.009423348121345043,
0.022629812359809875,
-0.030666174367070198,
-0.04010830447077751,
-0.008673428557813168,
0.01290692575275898,
-0.05678192898631096,
0.0162... |
51 | Graph-Based Simplex Method for Pairwise Energy Minimization With Binary Variables | [
"Daniel Prusa"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Prusa_Graph-Based_Simplex_Method_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Prusa_Graph-Based_Simplex_Method_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Prusa_2015_CVPR,author = {Prusa, Daniel},title = {Graph-Based Simplex Method for Pairwise Energy Minimization With Binary Variables},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We show how the simplex algorithm can be tailored to the linear programming relaxation of pairwise energy minimization with binary variables. A special structure formed by basic and nonbasic variables in each stage of the algorithm is identified and utilized to perform the whole iterative process combinatorially over t... | [
-0.00966142863035202,
-0.0016966063994914293,
0.0006358601385727525,
0.051590848714113235,
0.03010387159883976,
0.04985637590289116,
-0.004203444346785545,
0.005146813578903675,
-0.025995668023824692,
-0.04220062866806984,
-0.005350628402084112,
-0.0064000338315963745,
-0.06486029922962189,
... |
52 | Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples | [
"Hangfan Liu",
"Ruiqin Xiong",
"Jian Zhang",
"Wen Gao"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Liu_Image_Denoising_via_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Liu_Image_Denoising_via_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2015_CVPR,author = {Liu, Hangfan and Xiong, Ruiqin and Zhang, Jian and Gao, Wen},title = {Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper proposes a new image denoising approach using adaptive signal modeling and adaptive soft-thresholding. It improves the image quality by regularizing all the patches in image based on distribution modeling in transform domain. Instead of using a global model for all patches, it employs content adaptive models... | [
-0.0007012422429397702,
-0.01599685475230217,
0.03497330844402313,
0.022136464715003967,
0.042890433222055435,
0.04605122655630112,
0.011107804253697395,
0.0026027150452136993,
-0.042571403086185455,
-0.06741496175527573,
-0.019966835156083107,
0.024547487497329712,
-0.046304021030664444,
... |
53 | 3D Scanning Deformable Objects With a Single RGBD Sensor | [
"Mingsong Dou",
"Jonathan Taylor",
"Henry Fuchs",
"Andrew Fitzgibbon",
"Shahram Izadi"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Dou_3D_Scanning_Deformable_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Dou_3D_Scanning_Deformable_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Dou_2015_CVPR,author = {Dou, Mingsong and Taylor, Jonathan and Fuchs, Henry and Fitzgibbon, Andrew and Izadi, Shahram},title = {3D Scanning Deformable Objects With a Single RGBD Sensor},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | We present a 3D scanning system for deformable objects that uses only a single Kinect sensor. Our work allows considerable amount of nonrigid deformations during scanning, and achieves high quality results without heavily constraining user or camera motion. We do not rely on any prior shape knowledge, enabling general ... | [
-0.01964501477777958,
-0.01992858573794365,
0.008927530609071255,
0.013450001366436481,
0.04405663162469864,
0.06450150161981583,
0.0027021837886422873,
0.019659098237752914,
-0.022080354392528534,
-0.061049934476614,
-0.021916741505265236,
-0.02116740494966507,
-0.03621119260787964,
-0.00... |
54 | Nested Motion Descriptors | [
"Jeffrey Byrne"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Byrne_Nested_Motion_Descriptors_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Byrne_Nested_Motion_Descriptors_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Byrne_Nested_Motion_Descriptors_2015_CVPR_supplemental.zip | null | null | @InProceedings{Byrne_2015_CVPR,author = {Byrne, Jeffrey},title = {Nested Motion Descriptors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | A nested motion descriptor is a spatiotemporal representation of motion that is invariant to global camera translation, without requiring an explicit estimate of optical flow or camera stabilization. This descriptor is a natural spatiotemporal extension of the nested shape descriptor to the representation of motion. ... | [
0.02530193142592907,
0.011125510558485985,
0.031013859435915947,
-0.0011467779986560345,
0.036867592483758926,
0.03954512998461723,
0.017171138897538185,
0.01851203478872776,
-0.0489768348634243,
-0.04085306078195572,
-0.0045479401014745235,
-0.05167021602392197,
-0.045795291662216187,
0.0... |
55 | Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo | [
"Gottfried Graber",
"Jonathan Balzer",
"Stefano Soatto",
"Thomas Pock"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Graber_Efficient_Minimal-Surface_Regularization_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Graber_Efficient_Minimal-Surface_Regularization_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Graber_2015_CVPR,author = {Graber, Gottfried and Balzer, Jonathan and Soatto, Stefano and Pock, Thomas},title = {Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},mon... | We propose a method for dense three-dimensional surface reconstruction that leverages the strengths of shape-based approaches, by imposing regularization that respects the geometry of the surface, and the strength of depth-map-based stereo, by avoiding costly computation of surface topology. The result is a near real-t... | [
0.009758302010595798,
0.03664480149745941,
0.02420194074511528,
0.03519563004374504,
0.019019102677702904,
0.06556741148233414,
0.020535411313176155,
0.010118403472006321,
-0.04063166677951813,
-0.08151285350322723,
-0.04511454328894615,
0.0031692294869571924,
-0.04346201941370964,
0.02718... |
56 | Maximum Persistency via Iterative Relaxed Inference With Graphical Models | [
"Alexander Shekhovtsov",
"Paul Swoboda",
"Bogdan Savchynskyy"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Shekhovtsov_Maximum_Persistency_via_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Shekhovtsov_Maximum_Persistency_via_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Shekhovtsov_Maximum_Persistency_via_2015_CVPR_supplemental.zip | 1508.07902 | title_snapshot | @InProceedings{Shekhovtsov_2015_CVPR,author = {Shekhovtsov, Alexander and Swoboda, Paul and Savchynskyy, Bogdan},title = {Maximum Persistency via Iterative Relaxed Inference With Graphical Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2... | We consider MAP-inference for graphical models and propose a novel efficient algorithm for finding persistent labels. Our algorithm marks each label in each node of the considered graphical model either as (i) optimal, meaning that it belongs to all optimal solutions of the inference problem; (ii) non-optimal if it pro... | [
-0.018517285585403442,
-0.004860962275415659,
-0.008861428126692772,
0.06044955179095268,
0.01123865321278572,
0.01943526230752468,
0.03504093736410141,
0.02572014182806015,
-0.019677354022860527,
-0.0446980744600296,
-0.012597211636602879,
0.0033406796865165234,
-0.07729515433311462,
0.00... |
57 | Deep Hierarchical Parsing for Semantic Segmentation | [
"Abhishek Sharma",
"Oncel Tuzel",
"David W. Jacobs"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_supplemental.zip | 1503.02725 | title_snapshot | @InProceedings{Sharma_2015_CVPR,author = {Sharma, Abhishek and Tuzel, Oncel and Jacobs, David W.},title = {Deep Hierarchical Parsing for Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse ... | [
0.004502519499510527,
0.0010958209168165922,
0.025369398295879364,
0.052754562348127365,
0.02593667432665825,
0.038297612220048904,
0.010615367442369461,
0.015993932262063026,
-0.02972000651061535,
-0.03432511165738106,
-0.04680489003658295,
-0.021288715302944183,
-0.04907996207475662,
-0.... |
58 | Designing Deep Networks for Surface Normal Estimation | [
"Xiaolong Wang",
"David Fouhey",
"Abhinav Gupta"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Wang_Designing_Deep_Networks_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Wang_Designing_Deep_Networks_2015_CVPR_paper.pdf | null | 1411.4958 | title_snapshot | @InProceedings{Wang_2015_CVPR,author = {Wang, Xiaolong and Fouhey, David and Gupta, Abhinav},title = {Designing Deep Networks for Surface Normal Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture? We propose to build upon the decades of hard work in 3D scene understan... | [
0.012757210992276669,
0.018007708713412285,
0.014273814857006073,
0.0019782576709985733,
0.044234491884708405,
0.018195878714323044,
0.02324354276061058,
0.006843569688498974,
-0.0015585693763568997,
-0.08561921119689941,
0.011809567920863628,
-0.007748279720544815,
-0.05961361527442932,
-... |
59 | Layered RGBD Scene Flow Estimation | [
"Deqing Sun",
"Erik B. Sudderth",
"Hanspeter Pfister"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Sun_Layered_RGBD_Scene_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Sun_Layered_RGBD_Scene_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Sun_2015_CVPR,author = {Sun, Deqing and Sudderth, Erik B. and Pfister, Hanspeter},title = {Layered RGBD Scene Flow Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention. Although the depth information allows the recovery of 3D motion from a single view, it poses new challenges. In particular, depth boundaries are not well-aligned with RGB image edges and there... | [
-0.007719677407294512,
-0.005447064060717821,
0.023383740335702896,
0.016814781352877617,
0.005855492781847715,
0.03503256291151047,
0.012082856148481369,
0.006017783656716347,
-0.03163163736462593,
-0.028090713545680046,
-0.014477228745818138,
-0.015181009657680988,
-0.04177801311016083,
... |
60 | Hashing With Binary Autoencoders | [
"Miguel A. Carreira-Perpinan",
"Ramin Raziperchikolaei"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Carreira-Perpinan_Hashing_With_Binary_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Carreira-Perpinan_Hashing_With_Binary_2015_CVPR_paper.pdf | null | 1501.00756 | title_snapshot | @InProceedings{Carreira-Perpinan_2015_CVPR,author = {Carreira-Perpinan, Miguel A. and Raziperchikolaei, Ramin},title = {Hashing With Binary Autoencoders},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approac... | [
-0.0076483311131596565,
-0.023661866784095764,
-0.01974303461611271,
0.08146100491285324,
0.03928123787045479,
0.026380203664302826,
0.023479202762246132,
-0.0027617081068456173,
-0.027204236015677452,
-0.037415530532598495,
-0.04686778411269188,
-0.016171572729945183,
-0.050863899290561676,... |
61 | SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite | [
"Shuran Song",
"Samuel P. Lichtenberg",
"Jianxiong Xiao"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Song_SUN_RGB-D_A_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Song_SUN_RGB-D_A_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Song_2015_CVPR,author = {Song, Shuran and Lichtenberg, Samuel P. and Xiao, Jianxiong},title = {SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this pap... | [
0.0001455856836400926,
-0.009353148750960827,
0.0002964142768178135,
0.02466801181435585,
0.028023110702633858,
0.028433896601200104,
0.026383427903056145,
0.02313326857984066,
-0.02981862984597683,
-0.027913128957152367,
-0.01670241355895996,
0.0036346830893307924,
-0.05914333090186119,
-... |
62 | Collaborative Feature Learning From Social Media | [
"Chen Fang",
"Hailin Jin",
"Jianchao Yang",
"Zhe Lin"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Fang_Collaborative_Feature_Learning_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Fang_Collaborative_Feature_Learning_2015_CVPR_paper.pdf | null | 1502.01423 | title_snapshot | @InProceedings{Fang_2015_CVPR,author = {Fang, Chen and Jin, Hailin and Yang, Jianchao and Lin, Zhe},title = {Collaborative Feature Learning From Social Media},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labeled data. However, this paradigm requires large-scale category labels, which limits its applicability to domains where labels are hard to ob... | [
0.024331390857696533,
-0.03751178830862045,
-0.006869209464639425,
0.04335475340485573,
0.021998640149831772,
0.002596865175291896,
0.03185820206999779,
-0.0013748758938163519,
-0.01366713922470808,
0.0038335840217769146,
-0.03526201844215393,
-0.0199067872017622,
-0.0642334520816803,
0.01... |
63 | Diversity-Induced Multi-View Subspace Clustering | [
"Xiaochun Cao",
"Changqing Zhang",
"Huazhu Fu",
"Si Liu",
"Hua Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Cao_2015_CVPR,author = {Cao, Xiaochun and Zhang, Changqing and Fu, Huazhu and Liu, Si and Zhang, Hua},title = {Diversity-Induced Multi-View Subspace Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering... | [
0.0004958673380315304,
-0.008532950654625893,
0.016672411933541298,
0.04806988313794136,
0.025666750967502594,
0.034973520785570145,
0.024727560579776764,
-0.03643815591931343,
-0.02284192480146885,
-0.054228562861680984,
-0.03537099435925484,
-0.0004019798943772912,
-0.06973905116319656,
... |
64 | Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection | [
"Grant Van Horn",
"Steve Branson",
"Ryan Farrell",
"Scott Haber",
"Jessie Barry",
"Panos Ipeirotis",
"Pietro Perona",
"Serge Belongie"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Horn_Building_a_Bird_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Horn_Building_a_Bird_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Horn_2015_CVPR,author = {Van Horn, Grant and Branson, Steve and Farrell, Ryan and Haber, Scott and Barry, Jessie and Ipeirotis, Panos and Perona, Pietro and Belongie, Serge},title = {Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Co... | We introduce tools and methodologies to collect high quality, large scale fine-grained computer vision datasets using citizen scientists -- crowd annotators who are passionate and knowledgeable about specific domains such as birds or airplanes. We worked with citizen scientists and domain experts to collect NABirds, a... | [
0.01643517054617405,
-0.03658073768019676,
-0.04491026699542999,
0.04117438569664955,
0.0559416301548481,
0.021845072507858276,
0.02808387391269207,
0.012188105843961239,
-0.03240979462862015,
-0.03186574950814247,
-0.03783835098147392,
-0.034801043570041656,
-0.0913483127951622,
-0.001710... |
65 | Early Burst Detection for Memory-Efficient Image Retrieval | [
"Miaojing Shi",
"Yannis Avrithis",
"Herve Jegou"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Shi_Early_Burst_Detection_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Shi_Early_Burst_Detection_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Shi_2015_CVPR,author = {Shi, Miaojing and Avrithis, Yannis and Jegou, Herve},title = {Early Burst Detection for Memory-Efficient Image Retrieval},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Recent works show that image comparison based on local descriptors is corrupted by visual bursts, which tend to dominate the image similarity. The existing strategies, like power-law normalization, improve the results by discounting the contribution of visual bursts to the image similarity. In this paper, we propose t... | [
-0.005744368769228458,
-0.041441574692726135,
0.018326641991734505,
0.033967502415180206,
0.028769738972187042,
-0.008201953023672104,
-0.009767362847924232,
0.0316348671913147,
-0.04729514196515083,
-0.04272997006773949,
-0.05021608620882034,
0.003963280934840441,
-0.0527629628777504,
0.0... |
66 | Indoor Scene Structure Analysis for Single Image Depth Estimation | [
"Wei Zhuo",
"Mathieu Salzmann",
"Xuming He",
"Miaomiao Liu"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zhuo_Indoor_Scene_Structure_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhuo_Indoor_Scene_Structure_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Zhuo_2015_CVPR,author = {Zhuo, Wei and Salzmann, Mathieu and He, Xuming and Liu, Miaomiao},title = {Indoor Scene Structure Analysis for Single Image Depth Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities. Unlike previous approaches that only reason locally, we propose to exploit the global structure of the scene to estimate its depth. To this end, we introduce a hierarchical representation of the s... | [
0.0018122114706784487,
0.024987811222672462,
0.043304700404405594,
0.019244909286499023,
0.04870324581861496,
0.03863883018493652,
0.0422477200627327,
0.012784743681550026,
-0.040987178683280945,
-0.04769115522503853,
-0.015715617686510086,
-0.025366775691509247,
-0.06287171691656113,
-0.0... |
67 | Light Field Layer Matting | [
"Juliet Fiss",
"Brian Curless",
"Rick Szeliski"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Fiss_Light_Field_Layer_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Fiss_Light_Field_Layer_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Fiss_2015_CVPR,author = {Fiss, Juliet and Curless, Brian and Szeliski, Rick},title = {Light Field Layer Matting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we use matting to separate foreground layers from light fields captured with a plenoptic camera. We represent the input 4D light field as a 4D background light field, plus a 2D spatially varying foreground color layer with alpha. Our method can be used to both pull a fore- ground matte and estimate an oc... | [
0.01849672570824623,
0.030930686742067337,
0.02502700872719288,
0.007853967137634754,
0.03049592673778534,
0.005987518932670355,
-0.02885173074901104,
0.007092295214533806,
-0.0721474140882492,
-0.03311871364712715,
-0.021362705156207085,
-0.02220640331506729,
-0.06289517879486084,
-0.0016... |
68 | Depth Camera Tracking With Contour Cues | [
"Qian-Yi Zhou",
"Vladlen Koltun"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zhou_Depth_Camera_Tracking_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhou_Depth_Camera_Tracking_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Zhou_2015_CVPR,author = {Zhou, Qian-Yi and Koltun, Vladlen},title = {Depth Camera Tracking With Contour Cues},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We present an approach for tracking camera pose in real time given a stream of depth images. Existing algorithms are prone to drift in the presence of smooth surfaces that destabilize geometric alignment. We show that useful contour cues can be extracted from noisy and incomplete depth input. These cues are used to est... | [
0.026461239904165268,
-0.007759410422295332,
-0.01689826510846615,
0.05770593881607056,
0.014481417834758759,
0.047410063445568085,
0.014402847737073898,
0.030380509793758392,
-0.03558887541294098,
-0.05020279809832573,
-0.043271757662296295,
0.00465621380135417,
-0.04848027974367142,
-0.0... |
69 | Radial Distortion Homography | [
"Zuzana Kukelova",
"Jan Heller",
"Martin Bujnak",
"Tomas Pajdla"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Kukelova_Radial_Distortion_Homography_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Kukelova_Radial_Distortion_Homography_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Kukelova_2015_CVPR,author = {Kukelova, Zuzana and Heller, Jan and Bujnak, Martin and Pajdla, Tomas},title = {Radial Distortion Homography},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more. Ignoring the radial distortion component in homography e... | [
-0.006910645868629217,
0.042885176837444305,
-0.0003636764013208449,
0.03312528133392334,
0.037156667560338974,
0.049862757325172424,
0.007215968798846006,
0.018673818558454514,
-0.06679818779230118,
-0.04113709554076195,
-0.016333427280187607,
-0.0050460766069591045,
-0.06306663900613785,
... |
70 | Efficient Object Localization Using Convolutional Networks | [
"Jonathan Tompson",
"Ross Goroshin",
"Arjun Jain",
"Yann LeCun",
"Christoph Bregler"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Tompson_Efficient_Object_Localization_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Tompson_Efficient_Object_Localization_2015_CVPR_paper.pdf | null | 1411.4280 | title_snapshot | @InProceedings{Tompson_2015_CVPR,author = {Tompson, Jonathan and Goroshin, Ross and Jain, Arjun and LeCun, Yann and Bregler, Christoph},title = {Efficient Object Localization Using Convolutional Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training. These benefits of pooling co... | [
0.024149905890226364,
-0.016812827438116074,
-0.012806965969502926,
0.04532269015908241,
0.024739708751440048,
0.029892688617110252,
0.012221948243677616,
0.04459938779473305,
-0.024255158379673958,
-0.048027604818344116,
-0.006469385232776403,
-0.03800680488348007,
-0.07014497369527817,
-... |
71 | Just Noticeable Defocus Blur Detection and Estimation | [
"Jianping Shi",
"Li Xu",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Shi_Just_Noticeable_Defocus_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Shi_Just_Noticeable_Defocus_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Shi_2015_CVPR,author = {Shi, Jianping and Xu, Li and Jia, Jiaya},title = {Just Noticeable Defocus Blur Detection and Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not strong, the slight edge blurriness contains informative clues related to depth. We found existing blur de... | [
0.017689408734440804,
0.011373713612556458,
0.0259011872112751,
0.018357308581471443,
0.029412949457764626,
0.011118818074464798,
0.045661561191082,
0.025813410058617592,
-0.06860143691301346,
-0.045076772570610046,
-0.018760308623313904,
0.01740972511470318,
-0.048805251717567444,
-0.0113... |
72 | How Do We Use Our Hands? Discovering a Diverse Set of Common Grasps | [
"De-An Huang",
"Minghuang Ma",
"Wei-Chiu Ma",
"Kris M. Kitani"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_How_Do_We_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Huang_How_Do_We_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Huang_How_Do_We_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Huang_2015_CVPR,author = {Huang, De-An and Ma, Minghuang and Ma, Wei-Chiu and Kitani, Kris M.},title = {How Do We Use Our Hands? Discovering a Diverse Set of Common Grasps},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Our aim is to show how state-of-the-art computer vision techniques can be used to advance prehensile analysis (i.e., understanding the functionality of human hands). Prehensile analysis is a broad field of multi-disciplinary interest, where researchers painstakingly manually analyze hours of hand-object interaction vid... | [
0.00734722800552845,
0.015029342845082283,
-0.0390041284263134,
-0.009243747219443321,
0.062242232263088226,
0.053267236799001694,
0.021540643647313118,
0.012058047577738762,
-0.04194632172584534,
-0.05760050192475319,
-0.009665979072451591,
-0.03834950923919678,
-0.06593339145183563,
-0.0... |
73 | Rotating Your Face Using Multi-Task Deep Neural Network | [
"Junho Yim",
"Heechul Jung",
"ByungIn Yoo",
"Changkyu Choi",
"Dusik Park",
"Junmo Kim"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Yim_Rotating_Your_Face_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Yim_Rotating_Your_Face_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Yim_2015_CVPR,author = {Yim, Junho and Jung, Heechul and Yoo, ByungIn and Choi, Changkyu and Park, Dusik and Kim, Junmo},title = {Rotating Your Face Using Multi-Task Deep Neural Network},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year... | Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose- and illumination- invariant feature. Zhu et al. [26] changed all arbitrary pose and illumination images to the frontal view image to use for the invariant feature... | [
0.00003723803092725575,
0.000976843643002212,
-0.007244393695145845,
0.009486937895417213,
0.019745327532291412,
0.04061402007937431,
0.04360498487949371,
0.012159531936049461,
-0.012244955636560917,
-0.07535675913095474,
-0.008762714453041553,
-0.009689039550721645,
-0.09250836074352264,
... |
74 | Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks | [
"Maxime Oquab",
"Leon Bottou",
"Ivan Laptev",
"Josef Sivic"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Oquab_Is_Object_Localization_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Oquab_2015_CVPR,author = {Oquab, Maxime and Bottou, Leon and Laptev, Ivan and Sivic, Josef},title = {Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | Successful visual object recognition methods typically rely on training datasets containing lots of richly annotated images. Annotating object bounding boxes is both expensive and subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level lab... | [
0.0037220558151602745,
-0.009625325910747051,
-0.00007046935934340581,
0.03877834230661392,
0.01851135678589344,
0.021908923983573914,
-0.01599128171801567,
0.007913647219538689,
-0.020852703601121902,
-0.010424898006021976,
-0.02825613133609295,
0.008753280155360699,
-0.07238990068435669,
... |
75 | Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning | [
"Xiao-Yuan Jing",
"Xiaoke Zhu",
"Fei Wu",
"Xinge You",
"Qinglong Liu",
"Dong Yue",
"Ruimin Hu",
"Baowen Xu"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Jing_Super-Resolution_Person_Re-Identification_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Jing_Super-Resolution_Person_Re-Identification_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Jing_2015_CVPR,author = {Jing, Xiao-Yuan and Zhu, Xiaoke and Wu, Fei and You, Xinge and Liu, Qinglong and Yue, Dong and Hu, Ruimin and Xu, Baowen},title = {Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning},booktitle = {Proceedings of the IEEE Conferenc... | Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. ... | [
-0.013478613458573818,
-0.027149051427841187,
-0.0003156553430017084,
0.02565678022801876,
0.0765450969338417,
0.01506426278501749,
0.007587777450680733,
-0.044836945831775665,
-0.03354854881763458,
-0.04390762746334076,
-0.019081637263298035,
-0.010854884050786495,
-0.06800713390111923,
0... |
76 | Notice of Violation of IEEE Publication Principles: Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution | [
"Hang Yang",
"Ming Zhu",
"Yan Niu",
"Yujing Guan",
"Zhongbo Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Yang_Dual_Domain_Filters_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Yang_Dual_Domain_Filters_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Yang_Dual_Domain_Filters_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Yang_2015_CVPR,author = {Yang, Hang and Zhu, Ming and Niu, Yan and Guan, Yujing and Zhang, Zhongbo},title = {Notice of Violation of IEEE Publication Principles: Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution},booktitle = {Proceedings of the IEEE Conference on Com... | The following message is relayed from an update made on IEEE Xplore.
Notice of Violation of IEEE Publication Principles
"Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution"
by Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, and Zhongbo Zhang
in the Proceedings of the IEEE Conference o... | [
-0.0010925073875114322,
0.006214535795152187,
0.0076337032951414585,
0.03876590356230736,
0.049977902323007584,
-0.008630819618701935,
0.007436724845319986,
-0.013429654762148857,
-0.03780468925833702,
-0.05171862617135048,
-0.03205686807632446,
0.02302142046391964,
-0.04806775972247124,
0... |
77 | Region-Based Temporally Consistent Video Post-Processing | [
"Xuan Dong",
"Boyan Bonev",
"Yu Zhu",
"Alan L. Yuille"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Dong_Region-Based_Temporally_Consistent_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Dong_Region-Based_Temporally_Consistent_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Dong_2015_CVPR,author = {Dong, Xuan and Bonev, Boyan and Zhu, Yu and Yuille, Alan L.},title = {Region-Based Temporally Consistent Video Post-Processing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We study the problem of temporally consistent video post-processing. Previous post-processing algorithms usually either fail to keep high fidelity or fail to keep temporal consistency of output videos. In this paper, we observe experimentally that many image/video enhancement algorithms enforce a spatially consistent p... | [
0.054973166435956955,
-0.0025278411339968443,
-0.003845738945528865,
0.060304906219244,
0.04998614639043808,
0.03264247626066208,
0.017191508784890175,
0.02191818691790104,
-0.048176467418670654,
-0.07481860369443893,
-0.02880239486694336,
-0.012721158564090729,
-0.02746005728840828,
0.014... |
78 | Global Refinement of Random Forest | [
"Shaoqing Ren",
"Xudong Cao",
"Yichen Wei",
"Jian Sun"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Ren_Global_Refinement_of_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Ren_2015_CVPR,author = {Ren, Shaoqing and Cao, Xudong and Wei, Yichen and Sun, Jian},title = {Global Refinement of Random Forest},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniq... | [
0.022993184626102448,
-0.012216820381581783,
0.01622793637216091,
0.041746530681848526,
0.03760384023189545,
0.048990268260240555,
0.025400124490261078,
-0.0044384608045220375,
-0.011285905726253986,
-0.043330129235982895,
-0.006426754407584667,
-0.020883135497570038,
-0.07983908802270889,
... |
79 | Adaptive Region Pooling for Object Detection | [
"Yi-Hsuan Tsai",
"Onur C. Hamsici",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Tsai_Adaptive_Region_Pooling_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Tsai_Adaptive_Region_Pooling_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Tsai_Adaptive_Region_Pooling_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Tsai_2015_CVPR,author = {Tsai, Yi-Hsuan and Hamsici, Onur C. and Yang, Ming-Hsuan},title = {Adaptive Region Pooling for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity. We address this variability by a novel feature pooling method that is adaptive to segmented regions. The proposed detection algorithm automatically discovers a diver... | [
0.01808946393430233,
-0.004409281071275473,
0.008342595770955086,
0.042791981250047684,
0.03458110988140106,
0.0577511228621006,
0.002255385974422097,
0.022792130708694458,
-0.051593419164419174,
-0.051388196647167206,
-0.05121307820081711,
0.015527322888374329,
-0.04181781783699989,
-0.00... |
80 | Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning | [
"Mohammad Rastegari",
"Hannaneh Hajishirzi",
"Ali Farhadi"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Rastegari_Discriminative_and_Consistent_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Rastegari_Discriminative_and_Consistent_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Rastegari_2015_CVPR,author = {Rastegari, Mohammad and Hajishirzi, Hannaneh and Farhadi, Ali},title = {Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},... | In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are {\it hig... | [
0.005828553810715675,
0.011763133108615875,
0.012187658809125423,
0.05653906613588333,
0.025006620213389397,
0.03169550001621246,
0.01553463563323021,
-0.011864598840475082,
-0.028604503720998764,
0.001775422366335988,
-0.01963859610259533,
0.04619757458567619,
-0.08018708974123001,
0.0077... |
81 | MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking | [
"Zhibin Hong",
"Zhe Chen",
"Chaohui Wang",
"Xue Mei",
"Danil Prokhorov",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Hong_MUlti-Store_Tracker_MUSTer_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Hong_MUlti-Store_Tracker_MUSTer_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Hong_2015_CVPR,author = {Hong, Zhibin and Chen, Zhe and Wang, Chaohui and Mei, Xue and Prokhorov, Danil and Tao, Dacheng},title = {MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Reco... | Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance dur... | [
-0.011008556932210922,
-0.0023742762859910727,
0.015395790338516235,
0.012568874284625053,
0.04451926797628403,
0.03563288226723671,
0.014410460367798805,
0.0410444401204586,
-0.07656241953372955,
-0.07365737855434418,
-0.031361449509859085,
0.0005781434592790902,
-0.038115378469228745,
-0... |
82 | Finding Action Tubes | [
"Georgia Gkioxari",
"Jitendra Malik"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.pdf | null | 1411.6031 | title_snapshot | @InProceedings{Gkioxari_2015_CVPR,author = {Gkioxari, Georgia and Malik, Jitendra},title = {Finding Action Tubes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We address the problem of action detection in videos. Driven by the latest progress in object detection from 2D images, we build action models using rich feature hierarchies derived from shape and kinematic cues. We incorporate appearance and motion in two ways. First, starting from image region proposals we select tho... | [
0.031309958547353745,
-0.033402957022190094,
-0.006484375335276127,
0.04132890701293945,
0.011828610673546791,
0.0240579005330801,
0.03038245625793934,
0.023428747430443764,
0.002851444762200117,
-0.048918094485998154,
-0.023827681317925453,
-0.005829942878335714,
-0.05344758555293083,
0.0... |
83 | Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal | [
"Jian Sun",
"Wenfei Cao",
"Zongben Xu",
"Jean Ponce"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Sun_Learning_a_Convolutional_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Sun_Learning_a_Convolutional_2015_CVPR_paper.pdf | null | 1503.00593 | title_snapshot | @InProceedings{Sun_2015_CVPR,author = {Sun, Jian and Cao, Wenfei and Xu, Zongben and Ponce, Jean},title = {Learning a Convolutional Neural Network for Non-Uniform Motion Blur Removal},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motio... | [
0.03524869680404663,
0.0010921292705461383,
0.025103531777858734,
0.06131266430020332,
0.03722211346030235,
0.009975587017834187,
0.02567039243876934,
0.008612913079559803,
-0.01802401803433895,
-0.043472785502672195,
-0.030260080471634865,
-0.0019660729449242353,
-0.035166848450899124,
0.... |
84 | Complexity-Adaptive Distance Metric for Object Proposals Generation | [
"Yao Xiao",
"Cewu Lu",
"Efstratios Tsougenis",
"Yongyi Lu",
"Chi-Keung Tang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Xiao_Complexity-Adaptive_Distance_Metric_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Xiao_Complexity-Adaptive_Distance_Metric_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Xiao_Complexity-Adaptive_Distance_Metric_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Xiao_2015_CVPR,author = {Xiao, Yao and Lu, Cewu and Tsougenis, Efstratios and Lu, Yongyi and Tang, Chi-Keung},title = {Complexity-Adaptive Distance Metric for Object Proposals Generation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | Distance metric plays a key role in grouping superpixels to produce object proposals for object detection. We observe that existing distance metrics work primarily for low complexity cases. In this paper, we develop a novel distance metric for grouping two superpixels in high-complexity scenarios. Combining them, a com... | [
-0.03359906002879143,
-0.015760263428092003,
0.0038625006563961506,
0.017406495288014412,
0.02530802972614765,
0.05582346394658089,
-0.003426168579608202,
0.0031436122953891754,
-0.04639317840337753,
-0.05018061771988869,
-0.05327229201793671,
-0.010011250153183937,
-0.05307542905211449,
0... |
85 | High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild | [
"Xiangyu Zhu",
"Zhen Lei",
"Junjie Yan",
"Dong Yi",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zhu_High-Fidelity_Pose_and_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhu_High-Fidelity_Pose_and_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Zhu_High-Fidelity_Pose_and_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Zhu_2015_CVPR,author = {Zhu, Xiangyu and Lei, Zhen and Yan, Junjie and Yi, Dong and Li, Stan Z.},title = {High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance. An ideal normalization method is desired to be automatic, database independent and high-fidelity, where the face appearance should be preserved with l... | [
-0.01940024085342884,
0.02123103477060795,
0.0031225436832755804,
0.00537022203207016,
0.04631419852375984,
0.05150390788912773,
0.03524003550410271,
-0.03442273661494255,
-0.021943505853414536,
-0.05035388842225075,
-0.01493099331855774,
-0.015169753693044186,
-0.06327972561120987,
-0.007... |
86 | Transformation of Markov Random Fields for Marginal Distribution Estimation | [
"Masaki Saito",
"Takayuki Okatani"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Saito_Transformation_of_Markov_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Saito_Transformation_of_Markov_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Saito_Transformation_of_Markov_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Saito_2015_CVPR,author = {Saito, Masaki and Okatani, Takayuki},title = {Transformation of Markov Random Fields for Marginal Distribution Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | This paper presents a generic method for transforming MRFs for the marginal inference problem. Its major application is to downsize MRFs to speed up the computation. Unlike the MAP inference, there are only classical algorithms for the marginal inference problem such as BP etc. that require large computational cost. Al... | [
-0.009640236385166645,
0.00006673486495856196,
-0.00010940636275336146,
0.04349385201931,
0.07004250586032867,
0.05926702544093132,
0.012447201646864414,
0.0018540803575888276,
-0.032704662531614304,
-0.029836490750312805,
0.01798596978187561,
0.010291054844856262,
-0.0721920058131218,
0.0... |
87 | Sparse Convolutional Neural Networks | [
"Baoyuan Liu",
"Min Wang",
"Hassan Foroosh",
"Marshall Tappen",
"Marianna Pensky"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Liu_Sparse_Convolutional_Neural_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Liu_Sparse_Convolutional_Neural_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Liu_2015_CVPR,author = {Liu, Baoyuan and Wang, Min and Foroosh, Hassan and Tappen, Marshall and Pensky, Marianna},title = {Sparse Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtaine... | [
0.01236887276172638,
-0.01820267178118229,
0.015329490415751934,
0.04980859160423279,
0.035204142332077026,
0.03783484920859337,
-0.011039978824555874,
0.015029571019113064,
-0.04299142211675644,
-0.05384393781423569,
0.017830129712820053,
-0.004034444224089384,
-0.05968178063631058,
0.009... |
88 | FaceNet: A Unified Embedding for Face Recognition and Clustering | [
"Florian Schroff",
"Dmitry Kalenichenko",
"James Philbin"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf | null | 1503.03832 | title_snapshot | @InProceedings{Schroff_2015_CVPR,author = {Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},title = {FaceNet: A Unified Embedding for Face Recognition and Clustering},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Despite significant recent advances in the field of face recognition [DeepFace, DeepId2], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact... | [
0.021016499027609825,
-0.018177736550569534,
0.023446746170520782,
0.033412106335163116,
0.025181511417031288,
0.03948834165930748,
0.009165426716208458,
-0.00640215864405036,
-0.006810327991843224,
-0.04980624094605446,
0.015657978132367134,
-0.0058403899893164635,
-0.07327507436275482,
-... |
89 | Cascaded Hand Pose Regression | [
"Xiao Sun",
"Yichen Wei",
"Shuang Liang",
"Xiaoou Tang",
"Jian Sun"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Sun_Cascaded_Hand_Pose_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Sun_Cascaded_Hand_Pose_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Sun_2015_CVPR,author = {Sun, Xiao and Wei, Yichen and Liang, Shuang and Tang, Xiaoou and Sun, Jian},title = {Cascaded Hand Pose Regression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects. Our first contribution is 3D pose-indexed features that generalize the previous 2D parameterized features and achieve better invariance to 3D transformations. Our second contribution is... | [
-0.005366032011806965,
-0.010294460691511631,
0.01134567428380251,
0.0015235536266118288,
0.01786903850734234,
0.06734135746955872,
0.0248578954488039,
-0.008559245616197586,
-0.05134811997413635,
-0.0387323722243309,
0.004535361658781767,
-0.006941563915461302,
-0.06589879840612411,
-0.01... |
90 | Cross-Scene Crowd Counting via Deep Convolutional Neural Networks | [
"Cong Zhang",
"Hongsheng Li",
"Xiaogang Wang",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Cross-Scene_Crowd_Counting_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Cross-Scene_Crowd_Counting_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Zhang_2015_CVPR,author = {Zhang, Cong and Li, Hongsheng and Wang, Xiaogang and Yang, Xiaokang},title = {Cross-Scene Crowd Counting via Deep Convolutional Neural Networks},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this pr... | [
-0.0024330595042556524,
-0.0364474393427372,
0.025949405506253242,
-0.007380737457424402,
0.017187776044011116,
0.014715639874339104,
0.019497517496347427,
0.011919844895601273,
-0.035708844661712646,
-0.03354305773973465,
-0.0057442327961325645,
0.0008678314043208957,
-0.05382423847913742,
... |
91 | The Application of Two-Level Attention Models in Deep Convolutional Neural Network for Fine-Grained Image Classification | [
"Tianjun Xiao",
"Yichong Xu",
"Kuiyuan Yang",
"Jiaxing Zhang",
"Yuxin Peng",
"Zheng Zhang"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Xiao_The_Application_of_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Xiao_The_Application_of_2015_CVPR_paper.pdf | null | 1411.6447 | title_snapshot | @InProceedings{Xiao_2015_CVPR,author = {Xiao, Tianjun and Xu, Yichong and Yang, Kuiyuan and Zhang, Jiaxing and Peng, Yuxin and Zhang, Zheng},title = {The Application of Two-Level Attention Models in Deep Convolutional Neural Network for Fine-Grained Image Classification},booktitle = {Proceedings of the IEEE Conference ... | Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to ... | [
0.0004027593822684139,
0.00013612816110253334,
0.008565723896026611,
0.037022847682237625,
0.016279110684990883,
0.012968959286808968,
0.020432207733392715,
0.020897282287478447,
-0.0028495194856077433,
-0.03160642087459564,
-0.021914919838309288,
-0.002277539810165763,
-0.058168329298496246... |
92 | End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression | [
"Li Wan",
"David Eigen",
"Rob Fergus"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Wan_End-to-End_Integration_of_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf | null | 1411.5309 | title_judge | @InProceedings{Wan_2015_CVPR,author = {Wan, Li and Eigen, David and Fergus, Rob},title = {End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Deformable Parts Models and Convolutional Networks each have achieved notable performance in object detection. Yet these two approaches find their strengths in complementary areas: DPMs are well-versed in object composition, modeling fine-grained spatial relationships between parts; likewise, ConvNets are adept at pr... | [
-0.01099751703441143,
-0.006797120440751314,
-0.025825561955571175,
0.03547599911689758,
0.02939760684967041,
0.03748796135187149,
-0.014215529896318913,
0.010325223207473755,
-0.034378182142972946,
-0.05340629070997238,
-0.045287467539310455,
-0.009831872768700123,
-0.04818319156765938,
0... |
93 | A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions | [
"Kuan-Chuan Peng",
"Tsuhan Chen",
"Amir Sadovnik",
"Andrew C. Gallagher"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Peng_A_Mixed_Bag_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Peng_A_Mixed_Bag_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Peng_A_Mixed_Bag_2015_CVPR_supplemental.pdf | null | null | @InProceedings{Peng_2015_CVPR,author = {Peng, Kuan-Chuan and Chen, Tsuhan and Sadovnik, Amir and Gallagher, Andrew C.},title = {A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | This paper explores two new aspects of photos and human emotions. First, we show through psychovisual studies that different people have different emotional reactions to the same image, which is a strong and novel departure from previous work that only records and predicts a single dominant emotion for each image. Our ... | [
-0.011044294573366642,
0.01188333798199892,
0.001886972924694419,
0.02223087102174759,
0.026424609124660492,
0.023257741704583168,
0.009778137318789959,
0.014040266163647175,
-0.024844622239470482,
-0.04242300987243652,
-0.059620968997478485,
0.005440710112452507,
-0.06678584963083267,
-0.... |
94 | Neuroaesthetics in Fashion: Modeling the Perception of Fashionability | [
"Edgar Simo-Serra",
"Sanja Fidler",
"Francesc Moreno-Noguer",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Simo-Serra_Neuroaesthetics_in_Fashion_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Simo-Serra_Neuroaesthetics_in_Fashion_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Simo-Serra_2015_CVPR,author = {Simo-Serra, Edgar and Fidler, Sanja and Moreno-Noguer, Francesc and Urtasun, Raquel},title = {Neuroaesthetics in Fashion: Modeling the Perception of Fashionability},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | In this paper, we analyze the fashion of clothing of a large social website. Our goal is to learn and predict how fashionable a person looks on a photograph and suggest subtle improvements the user could make to improve her/his appeal. We propose a Conditional Random Field model that jointly reasons about several fashi... | [
0.03474976122379303,
-0.024387238547205925,
-0.010169342160224915,
0.019939275458455086,
0.04624709486961365,
0.02534356340765953,
0.03788616508245468,
0.004382899962365627,
-0.018618009984493256,
-0.03915083035826683,
-0.04325438663363457,
-0.0016447609523311257,
-0.04723992198705673,
-0.... |
95 | Part-Based Modelling of Compound Scenes From Images | [
"Anton van den Hengel",
"Chris Russell",
"Anthony Dick",
"John Bastian",
"Daniel Pooley",
"Lachlan Fleming",
"Lourdes Agapito"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Hengel_Part-Based_Modelling_of_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Hengel_Part-Based_Modelling_of_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Hengel_2015_CVPR,author = {van den Hengel, Anton and Russell, Chris and Dick, Anthony and Bastian, John and Pooley, Daniel and Fleming, Lachlan and Agapito, Lourdes},title = {Part-Based Modelling of Compound Scenes From Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Patter... | We propose a method to recover the structure of a compound scene from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined library, each with an associated pose. This has several advantages over a volume or mesh representation both for estimation and the utility of the... | [
0.024209044873714447,
-0.0005556638352572918,
-0.014439092017710209,
0.016636531800031662,
0.039628226310014725,
0.045443978160619736,
0.005545824766159058,
-0.00003342666241223924,
-0.058503542095422745,
-0.04693539813160896,
-0.028211170807480812,
-0.026998769491910934,
-0.0595817193388938... |
96 | Efficient Parallel Optimization for Potts Energy With Hierarchical Fusion | [
"Olga Veksler"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Veksler_Efficient_Parallel_Optimization_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Veksler_Efficient_Parallel_Optimization_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Veksler_2015_CVPR,author = {Veksler, Olga},title = {Efficient Parallel Optimization for Potts Energy With Hierarchical Fusion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Potts energy frequently occurs in computer vision applications. We present an efficient parallel method for optimizing Potts energy based on the extension of hierarchical fusion algorithm. Unlike previous parallel graph-cut based optimization algorithms, our approach has optimality bounds even after a single itera... | [
-0.02987194061279297,
0.007784368470311165,
0.006163116544485092,
0.056526023894548416,
0.027277810499072075,
0.06136954575777054,
0.012463914230465889,
0.010314255952835083,
-0.025722019374370575,
-0.05694717541337013,
0.003188294591382146,
-0.018521420657634735,
-0.0783756822347641,
0.00... |
97 | Pooled Motion Features for First-Person Videos | [
"Michael S. Ryoo",
"Brandon Rothrock",
"Larry Matthies"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.pdf | https://openaccess.thecvf.com/content_cvpr_2015/supplemental/Ryoo_Pooled_Motion_Features_2015_CVPR_supplemental.pdf | 1412.6505 | title_snapshot | @InProceedings{Ryoo_2015_CVPR,author = {Ryoo, Michael S. and Rothrock, Brandon and Matthies, Larry},title = {Pooled Motion Features for First-Person Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotion) and salient local motion observed in videos. We describe a representation framework based on time... | [
0.03239734470844269,
-0.018584975972771645,
0.0295540951192379,
0.0175120010972023,
0.0468960739672184,
0.022305497899651527,
0.012440689839422703,
0.027194930240511894,
-0.03647284209728241,
-0.031967248767614365,
-0.01601363904774189,
-0.029137637466192245,
-0.05948532745242119,
-0.01035... |
98 | Functional Correspondence by Matrix Completion | [
"Artiom Kovnatsky",
"Michael M. Bronstein",
"Xavier Bresson",
"Pierre Vandergheynst"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Kovnatsky_Functional_Correspondence_by_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Kovnatsky_Functional_Correspondence_by_2015_CVPR_paper.pdf | null | 1412.8070 | title_snapshot | @InProceedings{Kovnatsky_2015_CVPR,author = {Kovnatsky, Artiom and Bronstein, Michael M. and Bresson, Xavier and Vandergheynst, Pierre},title = {Functional Correspondence by Matrix Completion},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}... | In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional correspondence problem as matrix completion with manifold geometric structure and inducing functional localization with the L1 norm. We discuss ef... | [
0.0021022220607846975,
-0.003978910855948925,
0.02840382605791092,
0.012602284550666809,
0.03677903860807419,
0.07475164532661438,
0.00502144917845726,
0.017086409032344818,
-0.03614124283194542,
-0.07033306360244751,
-0.036002155393362045,
-0.007329149637371302,
-0.06609633564949036,
0.00... |
99 | Elastic-Net Regularization of Singular Values for Robust Subspace Learning | [
"Eunwoo Kim",
"Minsik Lee",
"Songhwai Oh"
] | https://openaccess.thecvf.com/content_cvpr_2015/html/Kim_Elastic-Net_Regularization_of_2015_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2015/papers/Kim_Elastic-Net_Regularization_of_2015_CVPR_paper.pdf | null | null | null | @InProceedings{Kim_2015_CVPR,author = {Kim, Eunwoo and Lee, Minsik and Oh, Songhwai},title = {Elastic-Net Regularization of Singular Values for Robust Subspace Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2015}} | Learning a low-dimensional structure plays an important role in computer vision. Recently, a new family of methods, such as l1 minimization and robust principal component analysis, has been proposed for low-rank matrix approximation problems and shown to be robust against outliers and missing data. But these methods of... | [
-0.02468365989625454,
-0.03934483230113983,
0.03218799829483032,
0.013471322134137154,
0.0431164875626564,
0.02687843143939972,
0.005481816362589598,
-0.01509010698646307,
-0.04779239371418953,
-0.053120773285627365,
-0.010332179255783558,
-0.0277692973613739,
-0.050020501017570496,
0.0020... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.