paper_id uint32 0 1.29k | title stringlengths 10 145 | authors listlengths 1 13 | cvf_url stringlengths 87 186 | pdf_url stringlengths 88 187 | supp_url stringlengths 102 141 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | bibtex large_stringlengths 233 517 | abstract large_stringlengths 530 2.03k | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Finding Task-Relevant Features for Few-Shot Learning by Category Traversal | [
"Hongyang Li",
"David Eigen",
"Samuel Dodge",
"Matthew Zeiler",
"Xiaogang Wang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Li_Finding_Task-Relevant_Features_for_Few-Shot_Learning_by_Category_Traversal_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Finding_Task-Relevant_Features_for_Few-Shot_Learning_by_Category_Traversal_CVPR_2019_paper.pdf | null | 1905.11116 | title_snapshot | @InProceedings{Li_2019_CVPR,author = {Li, Hongyang and Eigen, David and Dodge, Samuel and Zeiler, Matthew and Wang, Xiaogang},title = {Finding Task-Relevant Features for Few-Shot Learning by Category Traversal},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month ... | Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limited-example training situations are common practice. Recent effective approaches to few-shot learning employ a metric-learning framework to ... | [
0.0034392112866044044,
-0.030141115188598633,
-0.009447806514799595,
0.03725554421544075,
0.045856773853302,
0.013394983485341072,
0.05558239296078682,
-0.0021933522075414658,
-0.037263937294483185,
-0.002468261169269681,
-0.02971007488667965,
0.016019485890865326,
-0.05110941827297211,
-0... |
1 | Edge-Labeling Graph Neural Network for Few-Shot Learning | [
"Jongmin Kim",
"Taesup Kim",
"Sungwoong Kim",
"Chang D. Yoo"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Edge-Labeling_Graph_Neural_Network_for_Few-Shot_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Edge-Labeling_Graph_Neural_Network_for_Few-Shot_Learning_CVPR_2019_paper.pdf | null | 1905.01436 | title_snapshot | @InProceedings{Kim_2019_CVPR,author = {Kim, Jongmin and Kim, Taesup and Kim, Sungwoong and Yoo, Chang D.},title = {Edge-Labeling Graph Neural Network for Few-Shot Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-clu... | [
0.0009508392540737987,
-0.024401133880019188,
0.004697859287261963,
0.06424891948699951,
0.02399509586393833,
0.033204760402441025,
0.01815159246325493,
-0.004460209049284458,
-0.038121722638607025,
-0.03133566677570343,
-0.005214135628193617,
-0.00760365417227149,
-0.04748142138123512,
0.... |
2 | Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning | [
"Spyros Gidaris",
"Nikos Komodakis"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Gidaris_Generating_Classification_Weights_With_GNN_Denoising_Autoencoders_for_Few-Shot_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Gidaris_Generating_Classification_Weights_With_GNN_Denoising_Autoencoders_for_Few-Shot_Learning_CVPR_2019_paper.pdf | null | 1905.01102 | title_snapshot | @InProceedings{Gidaris_2019_CVPR,author = {Gidaris, Spyros and Komodakis, Nikos},title = {Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Given an initial recognition model already trained on a set of base classes, the goal of this work is to develop a meta-model for few-shot learning. The meta-model, given as input some novel classes with few training examples per class, must properly adapt the existing recognition model into a new model that can correc... | [
-0.0029444536194205284,
-0.02032286673784256,
-0.018582528457045555,
0.058149076998233795,
0.020883675664663315,
0.049512624740600586,
0.04964402690529823,
-0.001314881956204772,
-0.031104108318686485,
-0.02892274782061577,
-0.01721991039812565,
0.012366043403744698,
-0.0734555646777153,
-... |
3 | Kervolutional Neural Networks | [
"Chen Wang",
"Jianfei Yang",
"Lihua Xie",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Kervolutional_Neural_Networks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Kervolutional_Neural_Networks_CVPR_2019_paper.pdf | null | 1904.03955 | title_snapshot | @InProceedings{Wang_2019_CVPR,author = {Wang, Chen and Yang, Jianfei and Xie, Lihua and Yuan, Junsong},title = {Kervolutional Neural Networks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve th... | [
0.008673706091940403,
-0.026761000975966454,
0.043056920170784,
0.04192683845758438,
0.04270102456212044,
0.030311668291687965,
-0.0030371013563126326,
0.015147706493735313,
-0.006487034261226654,
-0.04602162167429924,
-0.021711912006139755,
-0.0029667920898646116,
-0.05476931110024452,
0.... |
4 | Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem | [
"Matthias Hein",
"Maksym Andriushchenko",
"Julian Bitterwolf"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Hein_Why_ReLU_Networks_Yield_High-Confidence_Predictions_Far_Away_From_the_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Hein_Why_ReLU_Networks_Yield_High-Confidence_Predictions_Far_Away_From_the_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Hein_Why_ReLU_Networks_CVPR_2019_supplemental.pdf | 1812.05720 | title_snapshot | @InProceedings{Hein_2019_CVPR,author = {Hein, Matthias and Andriushchenko, Maksym and Bitterwolf, Julian},title = {Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear cla... | [
0.005805199034512043,
-0.013473293744027615,
0.0021120160818099976,
0.060214921832084656,
0.04266982153058052,
0.03507592901587486,
0.02775862067937851,
-0.019871601834893227,
-0.004021582193672657,
-0.02226260118186474,
-0.017059706151485443,
-0.0011243483750149608,
-0.09869014471769333,
... |
5 | On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions | [
"Yusuke Tsuzuku",
"Issei Sato"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Tsuzuku_On_the_Structural_Sensitivity_of_Deep_Convolutional_Networks_to_the_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Tsuzuku_On_the_Structural_Sensitivity_of_Deep_Convolutional_Networks_to_the_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Tsuzuku_On_the_Structural_CVPR_2019_supplemental.pdf | 1809.04098 | title_snapshot | @InProceedings{Tsuzuku_2019_CVPR,author = {Tsuzuku, Yusuke and Sato, Issei},title = {On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {20... | Data-agnostic quasi-imperceptible perturbations on inputs are known to degrade recognition accuracy of deep convolutional networks severely. This phenomenon is considered to be a potential security issue. Moreover, some results on statistical generalization guarantees indicate that the phenomena can be a key to improve... | [
0.0010580167872831225,
-0.031536150723695755,
0.013959366828203201,
0.023897923529148102,
0.03349093720316887,
0.02123081684112549,
0.028833037242293358,
0.0061302888207137585,
-0.02778673730790615,
-0.03626595437526703,
-0.006253481842577457,
-0.013245351612567902,
-0.057181768119335175,
... |
6 | Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization | [
"Siyuan Qiao",
"Zhe Lin",
"Jianming Zhang",
"Alan L. Yuille"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Qiao_Neural_Rejuvenation_Improving_Deep_Network_Training_by_Enhancing_Computational_Resource_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Qiao_Neural_Rejuvenation_Improving_Deep_Network_Training_by_Enhancing_Computational_Resource_CVPR_2019_paper.pdf | null | 1812.00481 | title_snapshot | @InProceedings{Qiao_2019_CVPR,author = {Qiao, Siyuan and Lin, Zhe and Zhang, Jianming and Yuille, Alan L.},title = {Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | In this paper, we study the problem of improving computational resource utilization of neural networks. Deep neural networks are usually over-parameterized for their tasks in order to achieve good performances, thus are likely to have underutilized computational resources. This observation motivates a lot of research t... | [
-0.0016525813844054937,
-0.01789812371134758,
-0.0007919922936707735,
0.028244540095329285,
0.07754126936197281,
0.049368999898433685,
0.026331136003136635,
0.009324394166469574,
-0.022019093856215477,
-0.05319053307175636,
-0.02770928665995598,
-0.0031597448978573084,
-0.04576512426137924,
... |
7 | Hardness-Aware Deep Metric Learning | [
"Wenzhao Zheng",
"Zhaodong Chen",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Hardness-Aware_Deep_Metric_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zheng_Hardness-Aware_Deep_Metric_Learning_CVPR_2019_paper.pdf | null | 1903.05503 | title_snapshot | @InProceedings{Zheng_2019_CVPR,author = {Zheng, Wenzhao and Chen, Zhaodong and Lu, Jiwen and Zhou, Jie},title = {Hardness-Aware Deep Metric Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to cha... | [
0.015694867819547653,
-0.0032354993745684624,
0.014648945070803165,
0.030533015727996826,
0.041314054280519485,
0.05658150091767311,
0.008055012673139572,
-0.016565434634685516,
0.0019511198624968529,
-0.009506354108452797,
-0.020275553688406944,
0.013917452655732632,
-0.056078311055898666,
... |
8 | Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation | [
"Chenxi Liu",
"Liang-Chieh Chen",
"Florian Schroff",
"Hartwig Adam",
"Wei Hua",
"Alan L. Yuille",
"Li Fei-Fei"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Auto-DeepLab_Hierarchical_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Auto-DeepLab_Hierarchical_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2019_paper.pdf | null | 1901.02985 | title_snapshot | @InProceedings{Liu_2019_CVPR,author = {Liu, Chenxi and Chen, Liang-Chieh and Schroff, Florian and Adam, Hartwig and Hua, Wei and Yuille, Alan L. and Fei-Fei, Li},title = {Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS for semantic image segmentation. Existing works often focus on searching the repeatable cell structure, while hand-designin... | [
-0.011749912984669209,
-0.042782872915267944,
-0.009630435146391392,
0.022095797583460808,
0.022310147061944008,
0.0435154028236866,
0.027863066643476486,
-0.007225911132991314,
-0.012131145223975182,
-0.05002531036734581,
-0.03137195482850075,
-0.04182599484920502,
-0.023238949477672577,
... |
9 | Learning Loss for Active Learning | [
"Donggeun Yoo",
"In So Kweon"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Yoo_Learning_Loss_for_Active_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Yoo_Learning_Loss_for_Active_Learning_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Yoo_Learning_Loss_for_CVPR_2019_supplemental.pdf | 1905.03677 | title_snapshot | @InProceedings{Yoo_2019_CVPR,author = {Yoo, Donggeun and Kweon, In So},title = {Learning Loss for Active Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where a model asks human to annotate data that it perceived as uncertain. A variety of recent methods have been proposed to apply active learning ... | [
0.001701953704468906,
-0.027482539415359497,
-0.013171261176466942,
0.02589244768023491,
0.017229635268449783,
0.029704159125685692,
0.005740271415561438,
-0.014252256602048874,
-0.02490180730819702,
-0.037954602390527725,
-0.027754513546824455,
0.016538700088858604,
-0.06122254207730293,
... |
10 | Striking the Right Balance With Uncertainty | [
"Salman Khan",
"Munawar Hayat",
"Syed Waqas Zamir",
"Jianbing Shen",
"Ling Shao"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Khan_Striking_the_Right_Balance_With_Uncertainty_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Khan_Striking_the_Right_Balance_With_Uncertainty_CVPR_2019_paper.pdf | null | 1901.07590 | title_snapshot | @InProceedings{Khan_2019_CVPR,author = {Khan, Salman and Hayat, Munawar and Zamir, Syed Waqas and Shen, Jianbing and Shao, Ling},title = {Striking the Right Balance With Uncertainty},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Learning unbiased models on imbalanced datasets is a significant challenge. Rare classes tend to get a concentrated representation in the classification space which hampers the generalization of learned boundaries to new test examples. In this paper, we demonstrate that the Bayesian uncertainty estimates directly corre... | [
0.030090369284152985,
-0.014717581681907177,
-0.024704787880182266,
0.007807809393852949,
0.016307173296809196,
0.014694123528897762,
0.033871881663799286,
-0.005359061993658543,
-0.026096738874912262,
-0.06699489057064056,
-0.015544124878942966,
0.025300802662968636,
-0.08347774296998978,
... |
11 | AutoAugment: Learning Augmentation Strategies From Data | [
"Ekin D. Cubuk",
"Barret Zoph",
"Dandelion Mane",
"Vijay Vasudevan",
"Quoc V. Le"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Cubuk_AutoAugment_Learning_Augmentation_Strategies_From_Data_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Cubuk_AutoAugment_Learning_Augmentation_Strategies_From_Data_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Cubuk_AutoAugment_Learning_Augmentation_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Cubuk_2019_CVPR,author = {Cubuk, Ekin D. and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V.},title = {AutoAugment: Learning Augmentation Strategies From Data},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implement... | [
0.023161347955465317,
-0.055475689470767975,
-0.0037151265423744917,
0.05843876674771309,
0.036288242787122726,
0.024444371461868286,
0.018671546131372452,
-0.03305982053279877,
-0.031089305877685547,
-0.05841147154569626,
-0.05105455964803696,
-0.023997390642762184,
-0.08472727239131927,
... |
12 | SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences | [
"Huu M. Le",
"Thanh-Toan Do",
"Tuan Hoang",
"Ngai-Man Cheung"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Le_SDRSAC_Semidefinite-Based_Randomized_Approach_for_Robust_Point_Cloud_Registration_Without_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Le_SDRSAC_Semidefinite-Based_Randomized_Approach_for_Robust_Point_Cloud_Registration_Without_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Le_SDRSAC_Semidefinite-Based_Randomized_CVPR_2019_supplemental.pdf | 1904.03483 | title_snapshot | @InProceedings{Le_2019_CVPR,author = {Le, Huu M. and Do, Thanh-Toan and Hoang, Tuan and Cheung, Ngai-Man},title = {SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings... | [
0.01548058446496725,
-0.007121087517589331,
-0.015038812533020973,
0.04217364639043808,
0.01716042309999466,
0.06806676089763641,
-0.00039957795524969697,
0.007762583438307047,
-0.026213817298412323,
-0.06844883412122726,
-0.04664510861039162,
-0.06523267924785614,
-0.07574233412742615,
0.... |
13 | BAD SLAM: Bundle Adjusted Direct RGB-D SLAM | [
"Thomas Schops",
"Torsten Sattler",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Schops_BAD_SLAM_Bundle_Adjusted_Direct_RGB-D_SLAM_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Schops_BAD_SLAM_Bundle_Adjusted_Direct_RGB-D_SLAM_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Schops_BAD_SLAM_Bundle_CVPR_2019_supplemental.zip | null | null | @InProceedings{Schops_2019_CVPR,author = {Schops, Thomas and Sattler, Torsten and Pollefeys, Marc},title = {BAD SLAM: Bundle Adjusted Direct RGB-D SLAM},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | A key component of Simultaneous Localization and Mapping (SLAM) systems is the joint optimization of the estimated 3D map and camera trajectory. Bundle adjustment (BA) is the gold standard for this. Due to the large number of variables in dense RGB-D SLAM, previous work has focused on approximating BA. In contrast, in ... | [
0.009682722389698029,
0.011125637218356133,
0.007995040155947208,
0.016817843541502953,
0.03139905631542206,
0.05937676876783371,
0.03794622793793678,
0.020022345706820488,
-0.04177228361368179,
-0.039436712861061096,
-0.028605083003640175,
-0.027810562402009964,
-0.0939551368355751,
-0.01... |
14 | Revealing Scenes by Inverting Structure From Motion Reconstructions | [
"Francesco Pittaluga",
"Sanjeev J. Koppal",
"Sing Bing Kang",
"Sudipta N. Sinha"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Pittaluga_Revealing_Scenes_by_Inverting_Structure_From_Motion_Reconstructions_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Pittaluga_Revealing_Scenes_by_Inverting_Structure_From_Motion_Reconstructions_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Pittaluga_Revealing_Scenes_by_CVPR_2019_supplemental.pdf | 1904.03303 | title_snapshot | @InProceedings{Pittaluga_2019_CVPR,author = {Pittaluga, Francesco and Koppal, Sanjeev J. and Kang, Sing Bing and Sinha, Sudipta N.},title = {Revealing Scenes by Inverting Structure From Motion Reconstructions},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month =... | Many 3D vision systems localize cameras within a scene using 3D point clouds. Such point clouds are often obtained using structure from motion (SfM), after which the images are discarded to preserve privacy. In this paper, we show, for the first time, that such point clouds retain enough information to reveal scene app... | [
0.026338262483477592,
0.013541392982006073,
0.00788233894854784,
0.07480491697788239,
0.03369942680001259,
0.023132670670747757,
0.03085685893893242,
-0.004593638703227043,
-0.04384583979845047,
-0.037832506000995636,
-0.012893358245491982,
-0.05187472701072693,
-0.04650876671075821,
-0.00... |
15 | Strand-Accurate Multi-View Hair Capture | [
"Giljoo Nam",
"Chenglei Wu",
"Min H. Kim",
"Yaser Sheikh"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Nam_Strand-Accurate_Multi-View_Hair_Capture_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Nam_Strand-Accurate_Multi-View_Hair_Capture_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Nam_2019_CVPR,author = {Nam, Giljoo and Wu, Chenglei and Kim, Min H. and Sheikh, Yaser},title = {Strand-Accurate Multi-View Hair Capture},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Hair is one of the most challenging objects to reconstruct due to its micro-scale structure and a large number of repeated strands with heavy occlusions. In this paper, we present the first method to capture high-fidelity hair geometry with strand-level accuracy. Our method takes three stages to achieve this. In the fi... | [
0.0436854362487793,
-0.0033904938027262688,
-0.007963975891470909,
0.0234204214066267,
0.05632799491286278,
0.038387395441532135,
0.010384530760347843,
-0.0015338010853156447,
-0.018766356632113457,
-0.07689077407121658,
-0.01869068294763565,
-0.02021067962050438,
-0.05799008533358574,
-0.... |
16 | DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation | [
"Jeong Joon Park",
"Peter Florence",
"Julian Straub",
"Richard Newcombe",
"Steven Lovegrove"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Park_DeepSDF_Learning_Continuous_Signed_Distance_Functions_for_Shape_Representation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Park_DeepSDF_Learning_Continuous_Signed_Distance_Functions_for_Shape_Representation_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Park_DeepSDF_Learning_Continuous_CVPR_2019_supplemental.pdf | 1901.05103 | title_snapshot | @InProceedings{Park_2019_CVPR,author = {Park, Jeong Joon and Florence, Peter and Straub, Julian and Newcombe, Richard and Lovegrove, Steven},title = {DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Funct... | [
-0.000288837036350742,
-0.005581294652074575,
-0.016936833038926125,
0.053796034306287766,
0.03479545935988426,
0.06520210206508636,
0.0031803972087800503,
-0.005438674706965685,
-0.02507554367184639,
-0.0782407894730568,
0.0032373780850321054,
-0.006420770660042763,
-0.04633861780166626,
... |
17 | Pushing the Boundaries of View Extrapolation With Multiplane Images | [
"Pratul P. Srinivasan",
"Richard Tucker",
"Jonathan T. Barron",
"Ravi Ramamoorthi",
"Ren Ng",
"Noah Snavely"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Srinivasan_Pushing_the_Boundaries_of_View_Extrapolation_With_Multiplane_Images_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Srinivasan_Pushing_the_Boundaries_of_View_Extrapolation_With_Multiplane_Images_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Srinivasan_Pushing_the_Boundaries_CVPR_2019_supplemental.pdf | 1905.00413 | title_snapshot | @InProceedings{Srinivasan_2019_CVPR,author = {Srinivasan, Pratul P. and Tucker, Richard and Barron, Jonathan T. and Ramamoorthi, Ravi and Ng, Ren and Snavely, Noah},title = {Pushing the Boundaries of View Extrapolation With Multiplane Images},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating high-quality view extrapolations with plausible disocclusions. Our method builds upon prior work in predicting a multiplane image (MPI), which represents scene content as a set of RGBA planes within a reference view ... | [
-0.011551391333341599,
0.019623270258307457,
-0.0069108279421925545,
0.03898026421666145,
0.03753305599093437,
0.01860862784087658,
0.004181946162134409,
-0.0034298866521567106,
-0.026900840923190117,
-0.05583392456173897,
-0.001042080344632268,
-0.029577111825346947,
-0.07307326048612595,
... |
18 | GA-Net: Guided Aggregation Net for End-To-End Stereo Matching | [
"Feihu Zhang",
"Victor Prisacariu",
"Ruigang Yang",
"Philip H.S. Torr"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_GA-Net_Guided_Aggregation_Net_for_End-To-End_Stereo_Matching_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_GA-Net_Guided_Aggregation_Net_for_End-To-End_Stereo_Matching_CVPR_2019_paper.pdf | null | 1904.06587 | title_snapshot | @InProceedings{Zhang_2019_CVPR,author = {Zhang, Feihu and Prisacariu, Victor and Yang, Ruigang and Torr, Philip H.S.},title = {GA-Net: Guided Aggregation Net for End-To-End Stereo Matching},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019... | In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively. The first is a semi-global aggre... | [
0.010755744762718678,
-0.008001944050192833,
0.013494974933564663,
0.028016472235322,
0.009133514016866684,
0.055808812379837036,
-0.0009948263177648187,
-0.005328070372343063,
0.0026918393559753895,
-0.05753860995173454,
-0.022031648084521294,
-0.013018548488616943,
-0.1048041358590126,
-... |
19 | Real-Time Self-Adaptive Deep Stereo | [
"Alessio Tonioni",
"Fabio Tosi",
"Matteo Poggi",
"Stefano Mattoccia",
"Luigi Di Stefano"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Tonioni_Real-Time_Self-Adaptive_Deep_Stereo_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Tonioni_Real-Time_Self-Adaptive_Deep_Stereo_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Tonioni_Real-Time_Self-Adaptive_Deep_CVPR_2019_supplemental.pdf | 1810.05424 | title_snapshot | @InProceedings{Tonioni_2019_CVPR,author = {Tonioni, Alessio and Tosi, Fabio and Poggi, Matteo and Mattoccia, Stefano and Stefano, Luigi Di},title = {Real-Time Self-Adaptive Deep Stereo},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We a... | [
0.02698362059891224,
-0.010040787979960442,
-0.003184129949659109,
0.025515805929899216,
0.03669298440217972,
0.050635382533073425,
0.028665076941251755,
0.010294601321220398,
-0.003332508960738778,
-0.050582416355609894,
-0.03213287889957428,
0.020010830834507942,
-0.06122272089123726,
0.... |
20 | LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation | [
"Sunok Kim",
"Seungryong Kim",
"Dongbo Min",
"Kwanghoon Sohn"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Kim_LAF-Net_Locally_Adaptive_Fusion_Networks_for_Stereo_Confidence_Estimation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_LAF-Net_Locally_Adaptive_Fusion_Networks_for_Stereo_Confidence_Estimation_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Kim_2019_CVPR,author = {Kim, Sunok and Kim, Seungryong and Min, Dongbo and Sohn, Kwanghoon},title = {LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {20... | We present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks. The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps t... | [
0.02950146049261093,
0.007735982071608305,
0.018808068707585335,
0.06258042901754379,
0.016206301748752594,
0.06031814590096474,
0.014818055555224419,
0.013223015703260899,
-0.029790131375193596,
-0.05308831110596657,
-0.0012703726533800364,
0.025491606444120407,
-0.07572001218795776,
0.00... |
21 | NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences | [
"Chen Zhao",
"Zhiguo Cao",
"Chi Li",
"Xin Li",
"Jiaqi Yang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_NM-Net_Mining_Reliable_Neighbors_for_Robust_Feature_Correspondences_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_NM-Net_Mining_Reliable_Neighbors_for_Robust_Feature_Correspondences_CVPR_2019_paper.pdf | null | 1904.00320 | title_snapshot | @InProceedings{Zhao_2019_CVPR,author = {Zhao, Chen and Cao, Zhiguo and Li, Chi and Li, Xin and Yang, Jiaqi},title = {NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision. Searching spatially k-nearest neighbors is a common strategy for extracting local information in many previous works. However, there is no guarantee that the spatially k-nearest neighbors of correspondences are consiste... | [
0.005190329160541296,
-0.008273646235466003,
0.019990740343928337,
0.037948936223983765,
0.05271884799003601,
0.06075737997889519,
-0.0078018056228756905,
-0.005815428216010332,
-0.019623320549726486,
-0.05269405245780945,
-0.017030104994773865,
-0.003290031338110566,
-0.07254684716463089,
... |
22 | Coordinate-Free Carlsson-Weinshall Duality and Relative Multi-View Geometry | [
"Matthew Trager",
"Martial Hebert",
"Jean Ponce"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Trager_Coordinate-Free_Carlsson-Weinshall_Duality_and_Relative_Multi-View_Geometry_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Trager_Coordinate-Free_Carlsson-Weinshall_Duality_and_Relative_Multi-View_Geometry_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Trager_Coordinate-Free_Carlsson-Weinshall_Duality_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Trager_2019_CVPR,author = {Trager, Matthew and Hebert, Martial and Ponce, Jean},title = {Coordinate-Free Carlsson-Weinshall Duality and Relative Multi-View Geometry},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | We present a coordinate-free description of Carlsson-Weinshall duality between scene points and camera pinholes and use it to derive a new characterization of primal/dual multi-view geometry. In the case of three views, a particular set of reduced trilinearities provide a novel parameterization of camera geometry that,... | [
-0.019911939278244972,
0.03956626355648041,
0.03231065347790718,
0.023483658209443092,
0.03448641300201416,
0.018073592334985733,
-0.006320984102785587,
0.02595576085150242,
-0.050017181783914566,
-0.05582630634307861,
-0.05143440142273903,
-0.017288517206907272,
-0.06052675470709801,
0.00... |
23 | Deep Reinforcement Learning of Volume-Guided Progressive View Inpainting for 3D Point Scene Completion From a Single Depth Image | [
"Xiaoguang Han",
"Zhaoxuan Zhang",
"Dong Du",
"Mingdai Yang",
"Jingming Yu",
"Pan Pan",
"Xin Yang",
"Ligang Liu",
"Zixiang Xiong",
"Shuguang Cui"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Han_Deep_Reinforcement_Learning_of_Volume-Guided_Progressive_View_Inpainting_for_3D_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Han_Deep_Reinforcement_Learning_of_Volume-Guided_Progressive_View_Inpainting_for_3D_CVPR_2019_paper.pdf | null | 1903.04019 | title_snapshot | @InProceedings{Han_2019_CVPR,author = {Han, Xiaoguang and Zhang, Zhaoxuan and Du, Dong and Yang, Mingdai and Yu, Jingming and Pan, Pan and Yang, Xin and Liu, Ligang and Xiong, Zixiang and Cui, Shuguang},title = {Deep Reinforcement Learning of Volume-Guided Progressive View Inpainting for 3D Point Scene Completion From ... | We present a deep reinforcement learning method of progressive view inpainting for 3D point scene completion under volume guidance, achieving high-quality scene reconstruction from only a single depth image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2... | [
0.005203051492571831,
-0.01506511215120554,
0.0004945197142660618,
0.04760393127799034,
0.03273802623152733,
0.0340375155210495,
0.02354571595788002,
-0.004369194153696299,
-0.03497607633471489,
-0.08338325470685959,
-0.04362933710217476,
-0.01037685014307499,
-0.05454486235976219,
-0.0023... |
24 | Video Action Transformer Network | [
"Rohit Girdhar",
"Joao Carreira",
"Carl Doersch",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Girdhar_Video_Action_Transformer_Network_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Girdhar_Video_Action_Transformer_Network_CVPR_2019_paper.pdf | null | 1812.02707 | title_snapshot | @InProceedings{Girdhar_2019_CVPR,author = {Girdhar, Rohit and Carreira, Joao and Doersch, Carl and Zisserman, Andrew},title = {Video Action Transformer Network},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | We introduce the Action Transformer model for recognizing and localizing human actions in video clips. We repurpose a Transformer-style architecture to aggregate features from the spatiotemporal context around the person whose actions we are trying to classify. We show that by using high-resolution, person-specific, cl... | [
0.027304455637931824,
-0.02184799127280712,
-0.009121094830334187,
0.04012826830148697,
-0.003591387765482068,
0.003080250695347786,
0.01643027365207672,
0.007827351801097393,
-0.028626741841435432,
-0.01270917896181345,
-0.025208938866853714,
-0.00537756085395813,
-0.06231696158647537,
-0... |
25 | Timeception for Complex Action Recognition | [
"Noureldien Hussein",
"Efstratios Gavves",
"Arnold W.M. Smeulders"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Hussein_Timeception_for_Complex_Action_Recognition_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Hussein_Timeception_for_Complex_Action_Recognition_CVPR_2019_paper.pdf | null | 1812.01289 | title_snapshot | @InProceedings{Hussein_2019_CVPR,author = {Hussein, Noureldien and Gavves, Efstratios and Smeulders, Arnold W.M.},title = {Timeception for Complex Action Recognition},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | This paper focuses on the temporal aspect for recognizing human activities in videos; an important visual cue that has long been undervalued. We revisit the conventional definition of activity and restrict it to Complex Action: a set of one-actions with a weak temporal pattern that serves a specific purpose. Related wo... | [
0.01957317814230919,
-0.026280105113983154,
0.008333753794431686,
0.015619209036231041,
0.03051650896668434,
0.017848536372184753,
0.01943115144968033,
0.022494656965136528,
-0.010246668942272663,
-0.02238394320011139,
-0.0016329935751855373,
-0.005114986095577478,
-0.04380409047007561,
0.... |
26 | STEP: Spatio-Temporal Progressive Learning for Video Action Detection | [
"Xitong Yang",
"Xiaodong Yang",
"Ming-Yu Liu",
"Fanyi Xiao",
"Larry S. Davis",
"Jan Kautz"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Yang_STEP_Spatio-Temporal_Progressive_Learning_for_Video_Action_Detection_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_STEP_Spatio-Temporal_Progressive_Learning_for_Video_Action_Detection_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Yang_STEP_Spatio-Temporal_Progressive_CVPR_2019_supplemental.pdf | 1904.09288 | title_snapshot | @InProceedings{Yang_2019_CVPR,author = {Yang, Xitong and Yang, Xiaodong and Liu, Ming-Yu and Xiao, Fanyi and Davis, Larry S. and Kautz, Jan},title = {STEP: Spatio-Temporal Progressive Learning for Video Action Detection},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector--a progressive learning framework for spatio-temporal action detection in videos. Starting from a handful of coarse-scale proposal cuboids, our approach progressively refines the proposals towards actions over a few steps. In this way, high-qu... | [
0.03263261914253235,
-0.04378242790699005,
0.010082152672111988,
0.02838464453816414,
0.024655530229210854,
0.02073575370013714,
0.056089386343955994,
0.007590409833937883,
-0.005113163031637669,
-0.04476345330476761,
-0.004189001861959696,
-0.020601987838745117,
-0.04554571211338043,
-0.0... |
27 | Relational Action Forecasting | [
"Chen Sun",
"Abhinav Shrivastava",
"Carl Vondrick",
"Rahul Sukthankar",
"Kevin Murphy",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Relational_Action_Forecasting_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Relational_Action_Forecasting_CVPR_2019_paper.pdf | null | 1904.04231 | title_snapshot | @InProceedings{Sun_2019_CVPR,author = {Sun, Chen and Shrivastava, Abhinav and Vondrick, Carl and Sukthankar, Rahul and Murphy, Kevin and Schmid, Cordelia},title = {Relational Action Forecasting},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | This paper focuses on multi-person action forecasting in videos. More precisely, given a history of H previous frames, the goal is to detect actors and to predict their future actions for the next T frames. Our approach jointly models temporal and spatial interactions among different actors by constructing a recurrent ... | [
0.03896758705377579,
-0.01727035827934742,
0.014770813286304474,
0.024196019396185875,
0.004138115793466568,
0.01968107931315899,
0.04118916019797325,
0.004834766499698162,
-0.028014307841658592,
-0.01438431441783905,
-0.017943428829312325,
0.009288659319281578,
-0.07008900493383408,
-0.00... |
28 | Long-Term Feature Banks for Detailed Video Understanding | [
"Chao-Yuan Wu",
"Christoph Feichtenhofer",
"Haoqi Fan",
"Kaiming He",
"Philipp Krahenbuhl",
"Ross Girshick"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wu_Long-Term_Feature_Banks_for_Detailed_Video_Understanding_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Long-Term_Feature_Banks_for_Detailed_Video_Understanding_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Wu_Long-Term_Feature_Banks_CVPR_2019_supplemental.pdf | 1812.05038 | title_snapshot | @InProceedings{Wu_2019_CVPR,author = {Wu, Chao-Yuan and Feichtenhofer, Christoph and Fan, Haoqi and He, Kaiming and Krahenbuhl, Philipp and Girshick, Ross},title = {Long-Term Feature Banks for Detailed Video Understanding},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank--supportive information extracted over the entire span of a video--to augment state-of-the-art video models ... | [
0.01708313077688217,
-0.03949808329343796,
-0.0008005411946214736,
0.0412132628262043,
0.03915483132004738,
0.03440927714109421,
0.0060773082077503204,
0.032937292009592056,
-0.030789149925112724,
-0.03768106922507286,
-0.02879609540104866,
-0.02381461299955845,
-0.04753376543521881,
0.015... |
29 | Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes | [
"Yuke Li"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Li_Which_Way_Are_You_Going_Imitative_Decision_Learning_for_Path_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Which_Way_Are_You_Going_Imitative_Decision_Learning_for_Path_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Li_Which_Way_Are_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Li_2019_CVPR,author = {Li, Yuke},title = {Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Path forecasting is a pivotal step toward understanding dynamic scenes and an emerging topic in the computer vi- sion field. This task is challenging due to the multimodal nature of the future, namely, given a partial history, there is more than one plausible prediction. Yet, the state-of-the-art methods seem not fully... | [
-0.004339953418821096,
-0.04196510836482048,
0.011592688970267773,
0.017955658957362175,
0.04000895097851753,
0.005994234699755907,
0.036191970109939575,
0.04633777588605881,
-0.06282083690166473,
-0.041049595922231674,
-0.03988718241453171,
-0.006912156939506531,
-0.06273290514945984,
-0.... |
30 | What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment | [
"Paritosh Parmar",
"Brendan Tran Morris"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Parmar_What_and_How_Well_You_Performed_A_Multitask_Learning_Approach_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Parmar_What_and_How_Well_You_Performed_A_Multitask_Learning_Approach_CVPR_2019_paper.pdf | null | 1904.04346 | title_snapshot | @InProceedings{Parmar_2019_CVPR,author = {Parmar, Paritosh and Morris, Brendan Tran},title = {What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features ... | [
0.027097897604107857,
-0.005672452040016651,
-0.00583239458501339,
0.01826886087656021,
0.03705510124564171,
0.004325170535594225,
0.06591110676527023,
0.0048544518649578094,
-0.016136998310685158,
-0.029365818947553635,
0.005502238404005766,
0.009945418685674667,
-0.06605857610702515,
-0.... |
31 | MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation | [
"Shuangjie Xu",
"Daizong Liu",
"Linchao Bao",
"Wei Liu",
"Pan Zhou"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Xu_MHP-VOS_Multiple_Hypotheses_Propagation_for_Video_Object_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_MHP-VOS_Multiple_Hypotheses_Propagation_for_Video_Object_Segmentation_CVPR_2019_paper.pdf | null | 1904.08141 | title_snapshot | @InProceedings{Xu_2019_CVPR,author = {Xu, Shuangjie and Liu, Daizong and Bao, Linchao and Liu, Wei and Zhou, Pan},title = {MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | We address the problem of semi-supervised video object segmentation (VOS), where the masks of objects of interests are given in the first frame of an input video. To deal with challenging cases where objects are occluded or missing, previous work relies on greedy data association strategies that make decisions for each... | [
0.014719599857926369,
0.023989999666810036,
0.004669997841119766,
0.036312248557806015,
0.026001138612627983,
0.05100804194808006,
0.017298676073551178,
-0.016118986532092094,
-0.056123293936252594,
-0.05651801824569702,
-0.031741295009851456,
0.003971413243561983,
-0.05266466736793518,
0.... |
32 | 2.5D Visual Sound | [
"Ruohan Gao",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Gao_2.5D_Visual_Sound_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Gao_2.5D_Visual_Sound_CVPR_2019_paper.pdf | null | 1812.04204 | title_snapshot | @InProceedings{Gao_2019_CVPR,author = {Gao, Ruohan and Grauman, Kristen},title = {2.5D Visual Sound},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Binaural audio provides a listener with 3D sound sensation, allowing a rich perceptual experience of the scene. However, binaural recordings are scarcely available and require nontrivial expertise and equipment to obtain. We propose to convert common monaural audio into binaural audio by leveraging video. The key idea... | [
0.05214144289493561,
0.019217854365706444,
-0.003023311262950301,
0.037275515496730804,
0.020083677023649216,
0.048352859914302826,
0.04267586022615433,
0.01336652971804142,
-0.019230172038078308,
-0.0721224695444107,
0.011740726418793201,
0.026362473145127296,
-0.05204571411013603,
0.0137... |
33 | Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model | [
"Weining Wang",
"Yan Huang",
"Liang Wang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Language-Driven_Temporal_Activity_Localization_A_Semantic_Matching_Reinforcement_Learning_Model_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Language-Driven_Temporal_Activity_Localization_A_Semantic_Matching_Reinforcement_Learning_Model_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Wang_2019_CVPR,author = {Wang, Weining and Huang, Yan and Wang, Liang},title = {Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2... | Current studies on action detection in untrimmed videos are mostly designed for action classes, where an action is described at word level such as jumping, tumbling, swing, etc. This paper focuses on a rarely investigated problem of localizing an activity via a sentence query which would be more challenging and practic... | [
0.021824656054377556,
-0.02785407565534115,
-0.01939854770898819,
0.04714499041438103,
0.052137020975351334,
0.016849715262651443,
0.039483681321144104,
0.03244080767035484,
-0.02677045203745365,
-0.010252329520881176,
-0.02111836150288582,
0.01793372631072998,
-0.052963849157094955,
-0.02... |
34 | Gaussian Temporal Awareness Networks for Action Localization | [
"Fuchen Long",
"Ting Yao",
"Zhaofan Qiu",
"Xinmei Tian",
"Jiebo Luo",
"Tao Mei"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Long_Gaussian_Temporal_Awareness_Networks_for_Action_Localization_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Long_Gaussian_Temporal_Awareness_Networks_for_Action_Localization_CVPR_2019_paper.pdf | null | 1909.03877 | title_snapshot | @InProceedings{Long_2019_CVPR,author = {Long, Fuchen and Yao, Ting and Qiu, Zhaofan and Tian, Xinmei and Luo, Jiebo and Mei, Tao},title = {Gaussian Temporal Awareness Networks for Action Localization},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Temporally localizing actions in a video is a fundamental challenge in video understanding. Most existing approaches have often drawn inspiration from image object detection and extended the advances, e.g., SSD and Faster R-CNN, to produce temporal locations of an action in a 1D sequence. Nevertheless, the results can ... | [
0.04698437079787254,
-0.03434872627258301,
0.013094770722091198,
0.05490145832300186,
-0.010123112238943577,
0.02472902461886406,
0.03260186314582825,
0.017214028164744377,
-0.030484531074762344,
-0.02703145146369934,
-0.005919945426285267,
-0.020766468718647957,
-0.03716376796364784,
-0.0... |
35 | Efficient Video Classification Using Fewer Frames | [
"Shweta Bhardwaj",
"Mukundhan Srinivasan",
"Mitesh M. Khapra"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Bhardwaj_Efficient_Video_Classification_Using_Fewer_Frames_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Bhardwaj_Efficient_Video_Classification_Using_Fewer_Frames_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Bhardwaj_Efficient_Video_Classification_CVPR_2019_supplemental.pdf | 1902.10640 | title_snapshot | @InProceedings{Bhardwaj_2019_CVPR,author = {Bhardwaj, Shweta and Srinivasan, Mukundhan and Khapra, Mitesh M.},title = {Efficient Video Classification Using Fewer Frames},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Recently, there has been a lot of interest in building compact models for video classification which have a small memory footprint (<1 GB). While these models are compact, they typically operate by repeated application of a small weight matrix to all the frames in a video. For example, recurrent neural network based me... | [
0.01978776417672634,
-0.02551395446062088,
0.0022233983036130667,
0.05166926980018616,
0.03332548588514328,
0.030404895544052124,
-0.0026732017286121845,
0.0049848537892103195,
-0.04605215787887573,
-0.025384077802300453,
-0.0017102338606491685,
-0.009142538532614708,
-0.054578445851802826,
... |
36 | Parsing R-CNN for Instance-Level Human Analysis | [
"Lu Yang",
"Qing Song",
"Zhihui Wang",
"Ming Jiang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Parsing_R-CNN_for_Instance-Level_Human_Analysis_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Parsing_R-CNN_for_Instance-Level_Human_Analysis_CVPR_2019_paper.pdf | null | 1811.12596 | title_snapshot | @InProceedings{Yang_2019_CVPR,author = {Yang, Lu and Song, Qing and Wang, Zhihui and Jiang, Ming},title = {Parsing R-CNN for Instance-Level Human Analysis},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Instance-level human analysis is common in real-life scenarios and has multiple manifestations, such as human part segmentation, dense pose estimation, human-object interactions, etc. Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instanc... | [
0.00787095632404089,
-0.016437696292996407,
0.00371458288282156,
0.028763065114617348,
0.0210394449532032,
0.02889752760529518,
0.008342793211340904,
0.007231948431581259,
-0.019690468907356262,
-0.012124723754823208,
-0.02392163872718811,
-0.022412050515413284,
-0.06896096467971802,
-0.00... |
37 | Large Scale Incremental Learning | [
"Yue Wu",
"Yinpeng Chen",
"Lijuan Wang",
"Yuancheng Ye",
"Zicheng Liu",
"Yandong Guo",
"Yun Fu"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wu_Large_Scale_Incremental_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Large_Scale_Incremental_Learning_CVPR_2019_paper.pdf | null | 1905.13260 | title_snapshot | @InProceedings{Wu_2019_CVPR,author = {Wu, Yue and Chen, Yinpeng and Wang, Lijuan and Ye, Yuancheng and Liu, Zicheng and Guo, Yandong and Fu, Yun},title = {Large Scale Incremental Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}... | Modern machine learning suffers from catastrophic forgetting when learning new classes incrementally. The performance dramatically degrades due to the missing data of old classes. Incremental learning methods have been proposed to retain the knowledge acquired from the old classes, by using knowledge distilling and kee... | [
0.002325530629605055,
-0.05026677995920181,
0.0046777669340372086,
0.015788964927196503,
0.036141734570264816,
0.0033855936489999294,
0.017010491341352463,
-0.002451936714351177,
-0.05240752547979355,
-0.02852332778275013,
0.006226322613656521,
-0.01383812166750431,
-0.0869768038392067,
0.... |
38 | TopNet: Structural Point Cloud Decoder | [
"Lyne P. Tchapmi",
"Vineet Kosaraju",
"Hamid Rezatofighi",
"Ian Reid",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Tchapmi_TopNet_Structural_Point_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Tchapmi_2019_CVPR,author = {Tchapmi, Lyne P. and Kosaraju, Vineet and Rezatofighi, Hamid and Reid, Ian and Savarese, Silvio},title = {TopNet: Structural Point Cloud Decoder},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}... | 3D point cloud generation is of great use for 3D scene modeling and understanding. Real-world 3D object point clouds can be properly described by a collection of low-level and high-level structures such as surfaces, geometric primitives, semantic parts,etc. In fact, there exist many different representations ... | [
0.013799762353301048,
-0.00509671913459897,
0.02503817528486252,
0.05578229948878288,
0.022932732477784157,
0.0338897705078125,
-0.01054313313215971,
0.015610170550644398,
-0.03343833237886429,
-0.044117216020822525,
-0.04946740344166756,
-0.043309081345796585,
-0.05993937328457832,
0.0041... |
39 | Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification | [
"Yifan Sun",
"Qin Xu",
"Yali Li",
"Chi Zhang",
"Yikang Li",
"Shengjin Wang",
"Jian Sun"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Perceive_Where_to_Focus_Learning_Visibility-Aware_Part-Level_Features_for_Partial_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Perceive_Where_to_Focus_Learning_Visibility-Aware_Part-Level_Features_for_Partial_CVPR_2019_paper.pdf | null | 1904.00537 | title_snapshot | @InProceedings{Sun_2019_CVPR,author = {Sun, Yifan and Xu, Qin and Li, Yali and Zhang, Chi and Li, Yikang and Wang, Shengjin and Sun, Jian},title = {Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | This paper considers a realistic problem in person re-identification (re-ID) task, i.e., partial re-ID. Under partial re-ID scenario, the images may contain a partial observation of a pedestrian. If we directly compare a partial pedestrian image with a holistic one, the extreme spatial misalignment significantly compro... | [
0.002347308909520507,
-0.02598649263381958,
0.02811470441520214,
0.049759987741708755,
0.04284808412194252,
0.024901114404201508,
0.05092507228255272,
0.014534828253090382,
-0.053076550364494324,
-0.03578929975628853,
-0.04550357162952423,
-0.010202828794717789,
-0.0507088303565979,
-0.002... |
40 | Meta-Transfer Learning for Few-Shot Learning | [
"Qianru Sun",
"Yaoyao Liu",
"Tat-Seng Chua",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Meta-Transfer_Learning_for_Few-Shot_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Meta-Transfer_Learning_for_Few-Shot_Learning_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Sun_Meta-Transfer_Learning_for_CVPR_2019_supplemental.pdf | 1812.02391 | title_snapshot | @InProceedings{Sun_2019_CVPR,author = {Sun, Qianru and Liu, Yaoyao and Chua, Tat-Seng and Schiele, Bernt},title = {Meta-Transfer Learning for Few-Shot Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to... | [
0.009114793501794338,
-0.020220737904310226,
-0.00507488613948226,
0.0285699050873518,
0.039804499596357346,
0.01763167232275009,
0.04742203280329704,
0.013991489075124264,
-0.03299203887581825,
-0.009662515483796597,
-0.00466124527156353,
0.03227069228887558,
-0.05604660511016846,
-0.0063... |
41 | Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation | [
"Bohan Zhuang",
"Chunhua Shen",
"Mingkui Tan",
"Lingqiao Liu",
"Ian Reid"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhuang_Structured_Binary_Neural_Networks_for_Accurate_Image_Classification_and_Semantic_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhuang_Structured_Binary_Neural_Networks_for_Accurate_Image_Classification_and_Semantic_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Zhuang_Structured_Binary_Neural_CVPR_2019_supplemental.pdf | 1811.10413 | title_snapshot | @InProceedings{Zhuang_2019_CVPR,author = {Zhuang, Bohan and Shen, Chunhua and Tan, Mingkui and Liu, Lingqiao and Reid, Ian},title = {Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically for mobile devices with limited power capacity and computation resources. By assuming the same architecture to full-precision networks, previous works on quantizi... | [
-0.0063856919296085835,
-0.024656036868691444,
0.006822096649557352,
0.04068095237016678,
0.040218453854322433,
0.03576158359646797,
-0.004734293557703495,
-0.0035627284087240696,
-0.03187784552574158,
-0.03513791784644127,
-0.023412862792611122,
-0.014150113798677921,
-0.06573916226625443,
... |
42 | Deep RNN Framework for Visual Sequential Applications | [
"Bo Pang",
"Kaiwen Zha",
"Hanwen Cao",
"Chen Shi",
"Cewu Lu"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Pang_Deep_RNN_Framework_for_Visual_Sequential_Applications_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Pang_Deep_RNN_Framework_for_Visual_Sequential_Applications_CVPR_2019_paper.pdf | null | 1811.09961 | title_snapshot | @InProceedings{Pang_2019_CVPR,author = {Pang, Bo and Zha, Kaiwen and Cao, Hanwen and Shi, Chen and Lu, Cewu},title = {Deep RNN Framework for Visual Sequential Applications},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Extracting temporal and representation features efficiently plays a pivotal role in understanding visual sequence information. To deal with this, we propose a new recurrent neural framework that can be stacked deep effectively. There are mainly two novel designs in our deep RNN framework: one is a new RNN module called... | [
0.015150442719459534,
-0.019954124465584755,
0.005431713070720434,
0.044739533215761185,
0.0367785207927227,
0.05112559720873833,
0.02341143600642681,
0.043801743537187576,
-0.05075938254594803,
-0.042245376855134964,
0.022053079679608345,
0.016266433522105217,
-0.05740032717585564,
0.0023... |
43 | Graph-Based Global Reasoning Networks | [
"Yunpeng Chen",
"Marcus Rohrbach",
"Zhicheng Yan",
"Yan Shuicheng",
"Jiashi Feng",
"Yannis Kalantidis"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Graph-Based_Global_Reasoning_Networks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Graph-Based_Global_Reasoning_Networks_CVPR_2019_paper.pdf | null | 1811.12814 | title_snapshot | @InProceedings{Chen_2019_CVPR,author = {Chen, Yunpeng and Rohrbach, Marcus and Yan, Zhicheng and Shuicheng, Yan and Feng, Jiashi and Kalantidis, Yannis},title = {Graph-Based Global Reasoning Networks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant re... | [
0.012465574778616428,
-0.0041817654855549335,
0.024709856137633324,
0.05967884883284569,
0.03606095537543297,
0.026087790727615356,
-0.001016877475194633,
0.020068582147359848,
0.004040555562824011,
-0.024648623540997505,
-0.005860460456460714,
0.008918628096580505,
-0.09094813466072083,
0... |
44 | SSN: Learning Sparse Switchable Normalization via SparsestMax | [
"Wenqi Shao",
"Tianjian Meng",
"Jingyu Li",
"Ruimao Zhang",
"Yudian Li",
"Xiaogang Wang",
"Ping Luo"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Shao_SSN_Learning_Sparse_Switchable_Normalization_via_SparsestMax_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Shao_SSN_Learning_Sparse_Switchable_Normalization_via_SparsestMax_CVPR_2019_paper.pdf | null | 1903.03793 | title_snapshot | @InProceedings{Shao_2019_CVPR,author = {Shao, Wenqi and Meng, Tianjian and Li, Jingyu and Zhang, Ruimao and Li, Yudian and Wang, Xiaogang and Luo, Ping},title = {SSN: Learning Sparse Switchable Normalization via SparsestMax},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softma... | [
-0.005729056429117918,
-0.06544375419616699,
-0.004196227062493563,
0.0061185527592897415,
0.032837603241205215,
0.025808896869421005,
-0.0034639951772987843,
0.0030895303934812546,
-0.025149444118142128,
-0.06658226251602173,
0.0065135774202644825,
0.0001519280776847154,
-0.0416043363511562... |
45 | Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition | [
"Yongming Rao",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Rao_Spherical_Fractal_Convolutional_Neural_Networks_for_Point_Cloud_Recognition_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Rao_Spherical_Fractal_Convolutional_Neural_Networks_for_Point_Cloud_Recognition_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Rao_Spherical_Fractal_Convolutional_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Rao_2019_CVPR,author = {Rao, Yongming and Lu, Jiwen and Zhou, Jie},title = {Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | We present a generic, flexible and 3D rotation invariant framework based on spherical symmetry for point cloud recognition. By introducing regular icosahedral lattice and its fractals to approximate and discretize sphere, convolution can be easily implemented to process 3D points. Based on the fractal structure, a hie... | [
0.015764378011226654,
0.016143739223480225,
0.01437230035662651,
0.014660819433629513,
0.012521287426352501,
0.0605824775993824,
0.020449262112379074,
0.014052329584956169,
-0.049338195472955704,
-0.04862125590443611,
-0.025599857792258263,
-0.045485448092222214,
-0.0757979303598404,
0.023... |
46 | Learning to Generate Synthetic Data via Compositing | [
"Shashank Tripathi",
"Siddhartha Chandra",
"Amit Agrawal",
"Ambrish Tyagi",
"James M. Rehg",
"Visesh Chari"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Tripathi_Learning_to_Generate_Synthetic_Data_via_Compositing_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Tripathi_Learning_to_Generate_Synthetic_Data_via_Compositing_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Tripathi_Learning_to_Generate_CVPR_2019_supplemental.pdf | 1904.05475 | title_snapshot | @InProceedings{Tripathi_2019_CVPR,author = {Tripathi, Shashank and Chandra, Siddhartha and Agrawal, Amit and Tyagi, Ambrish and Rehg, James M. and Chari, Visesh},title = {Learning to Generate Synthetic Data via Compositing},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | We present a task-specific approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by assessing the strengths and weaknesses of a 'target' classifier. The synthesizer and target networks are trained in an adversarial manner wh... | [
0.038204945623874664,
-0.019914349541068077,
0.003024522913619876,
0.054516732692718506,
0.03199324011802673,
0.03155034780502319,
0.015319098718464375,
0.0012867609038949013,
-0.04617031663656235,
-0.043962083756923676,
-0.044322121888399124,
-0.007486455142498016,
-0.07868894189596176,
-... |
47 | Divide and Conquer the Embedding Space for Metric Learning | [
"Artsiom Sanakoyeu",
"Vadim Tschernezki",
"Uta Buchler",
"Bjorn Ommer"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Sanakoyeu_Divide_and_Conquer_the_Embedding_Space_for_Metric_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Sanakoyeu_Divide_and_Conquer_the_Embedding_Space_for_Metric_Learning_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Sanakoyeu_Divide_and_Conquer_CVPR_2019_supplemental.pdf | 1906.05990 | title_snapshot | @InProceedings{Sanakoyeu_2019_CVPR,author = {Sanakoyeu, Artsiom and Tschernezki, Vadim and Buchler, Uta and Ommer, Bjorn},title = {Divide and Conquer the Embedding Space for Metric Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {201... | Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications. Existing approaches usually learn a single metric in the embedding space for all available data points, which may have a very complex non... | [
-0.008512516506016254,
-0.009348663501441479,
-0.014946353621780872,
0.03540025278925896,
0.04685046151280403,
0.03370317071676254,
0.011332373134791851,
-0.015208126045763493,
0.0003372492210473865,
-0.030794011428952217,
-0.010861153714358807,
-0.022258136421442032,
-0.09477955102920532,
... |
48 | Latent Space Autoregression for Novelty Detection | [
"Davide Abati",
"Angelo Porrello",
"Simone Calderara",
"Rita Cucchiara"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Abati_Latent_Space_Autoregression_for_Novelty_Detection_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Abati_Latent_Space_Autoregression_for_Novelty_Detection_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Abati_Latent_Space_Autoregression_CVPR_2019_supplemental.pdf | 1807.01653 | title_snapshot | @InProceedings{Abati_2019_CVPR,author = {Abati, Davide and Porrello, Angelo and Calderara, Simone and Cucchiara, Rita},title = {Latent Space Autoregression for Novelty Detection},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Novelty detection is commonly referred as the discrimination of observations that do not conform to a learned model of regularity. Despite its importance in different application settings, designing a novelty detector is utterly complex due to the unpredictable nature of novelties and its inaccessibility during the tra... | [
0.03354848548769951,
-0.037270765751600266,
-0.009626459330320358,
0.04117147997021675,
0.06539570540189743,
0.026216896250844002,
0.03239143639802933,
-0.008725602179765701,
-0.027032742276787758,
-0.06575366109609604,
-0.028741221874952316,
-0.0017592739313840866,
-0.05117971450090408,
-... |
49 | Attending to Discriminative Certainty for Domain Adaptation | [
"Vinod Kumar Kurmi",
"Shanu Kumar",
"Vinay P. Namboodiri"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Kurmi_Attending_to_Discriminative_Certainty_for_Domain_Adaptation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Kurmi_Attending_to_Discriminative_Certainty_for_Domain_Adaptation_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Kurmi_Attending_to_Discriminative_CVPR_2019_supplemental.pdf | 1906.03502 | title_snapshot | @InProceedings{Kurmi_2019_CVPR,author = {Kurmi, Vinod Kumar and Kumar, Shanu and Namboodiri, Vinay P.},title = {Attending to Discriminative Certainty for Domain Adaptation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approache... | [
0.017013516277074814,
-0.01501647848635912,
0.019391365349292755,
0.05042657256126404,
0.04213468357920647,
0.03276703879237175,
0.03615955635905266,
-0.01658262498676777,
-0.0261649452149868,
-0.012699573300778866,
-0.057034995406866074,
0.022767994552850723,
-0.07335390150547028,
0.00130... |
50 | Feature Denoising for Improving Adversarial Robustness | [
"Cihang Xie",
"Yuxin Wu",
"Laurens van der Maaten",
"Alan L. Yuille",
"Kaiming He"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Xie_Feature_Denoising_for_Improving_Adversarial_Robustness_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Xie_Feature_Denoising_for_Improving_Adversarial_Robustness_CVPR_2019_paper.pdf | null | 1812.03411 | title_snapshot | @InProceedings{Xie_2019_CVPR,author = {Xie, Cihang and Wu, Yuxin and Maaten, Laurens van der and Yuille, Alan L. and He, Kaiming},title = {Feature Denoising for Improving Adversarial Robustness},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = ... | Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network archi... | [
-0.007524827029556036,
-0.02045278251171112,
-0.013299849815666676,
0.06162508204579353,
0.019101176410913467,
0.04272220656275749,
0.03935535252094269,
-0.022717058658599854,
-0.018167365342378616,
-0.054765164852142334,
-0.012472267262637615,
-0.02880878560245037,
-0.06442858278751373,
0... |
51 | Selective Kernel Networks | [
"Xiang Li",
"Wenhai Wang",
"Xiaolin Hu",
"Jian Yang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Li_Selective_Kernel_Networks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Selective_Kernel_Networks_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Li_Selective_Kernel_Networks_CVPR_2019_supplemental.pdf | 1903.06586 | title_snapshot | @InProceedings{Li_2019_CVPR,author = {Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Yang, Jian},title = {Selective Kernel Networks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in cons... | [
-0.02320675365626812,
-0.023645661771297455,
0.029915902763605118,
0.025776132941246033,
0.024795809760689735,
0.048707153648138046,
0.008226701989769936,
0.034015871584415436,
-0.05419619381427765,
-0.04432360827922821,
-0.024256465956568718,
-0.012431922368705273,
-0.06875139474868774,
0... |
52 | On Implicit Filter Level Sparsity in Convolutional Neural Networks | [
"Dushyant Mehta",
"Kwang In Kim",
"Christian Theobalt"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Mehta_On_Implicit_Filter_Level_Sparsity_in_Convolutional_Neural_Networks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Mehta_On_Implicit_Filter_Level_Sparsity_in_Convolutional_Neural_Networks_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Mehta_On_Implicit_Filter_CVPR_2019_supplemental.pdf | 1811.12495 | title_snapshot | @InProceedings{Mehta_2019_CVPR,author = {Mehta, Dushyant and Kim, Kwang In and Theobalt, Christian},title = {On Implicit Filter Level Sparsity in Convolutional Neural Networks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay. We conduct an extensive experimental study casting our initial findings into hy... | [
0.011377723887562752,
-0.01986880414187908,
-0.0003554642025846988,
0.03391898050904274,
0.03493501618504524,
0.021689685061573982,
0.0202878899872303,
0.01702270098030567,
-0.04676569998264313,
-0.05133466050028801,
0.013544649817049503,
-0.0024154307320713997,
-0.0494626984000206,
0.0147... |
53 | FlowNet3D: Learning Scene Flow in 3D Point Clouds | [
"Xingyu Liu",
"Charles R. Qi",
"Leonidas J. Guibas"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Liu_FlowNet3D_Learning_Scene_Flow_in_3D_Point_Clouds_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_FlowNet3D_Learning_Scene_Flow_in_3D_Point_Clouds_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Liu_FlowNet3D_Learning_Scene_CVPR_2019_supplemental.pdf | 1806.01411 | title_snapshot | @InProceedings{Liu_2019_CVPR,author = {Liu, Xingyu and Qi, Charles R. and Guibas, Leonidas J.},title = {FlowNet3D: Learning Scene Flow in 3D Point Clouds},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Many applications in robotics and human-computer interaction can benefit from understanding 3D motion of points in a dynamic environment, widely noted as scene flow. While most previous methods focus on stereo and RGB-D images as input, few try to estimate scene flow directly from point clouds. In this work, we propose... | [
0.027226433157920837,
0.0013461961643770337,
0.019474806264042854,
0.043051160871982574,
0.020770715549588203,
0.03747645020484924,
0.010986650362610817,
0.012947852723300457,
-0.02917599305510521,
-0.020621545612812042,
-0.04190121218562126,
-0.03197154402732849,
-0.05878881737589836,
-0.... |
54 | Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks | [
"Kuan Fang",
"Alexander Toshev",
"Li Fei-Fei",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Fang_Scene_Memory_Transformer_for_Embodied_Agents_in_Long-Horizon_Tasks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Fang_Scene_Memory_Transformer_for_Embodied_Agents_in_Long-Horizon_Tasks_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Fang_Scene_Memory_Transformer_CVPR_2019_supplemental.pdf | 1903.03878 | title_snapshot | @InProceedings{Fang_2019_CVPR,author = {Fang, Kuan and Toshev, Alexander and Fei-Fei, Li and Savarese, Silvio},title = {Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Many robotic applications require the agent to perform long-horizon tasks in partially observable environments. In such applications, decision making at any step can depend on observations received far in the past. Hence, being able to properly memorize and utilize the long-term history is crucial. In this work, we pro... | [
-0.03737163916230202,
-0.020980114117264748,
-0.006408107932657003,
0.024236440658569336,
0.025784170255064964,
0.014023498632013798,
-0.0003047304635401815,
0.02544461376965046,
-0.05170847475528717,
-0.0024452609941363335,
-0.02795209363102913,
0.02432282269001007,
-0.08370565623044968,
... |
55 | Co-Occurrent Features in Semantic Segmentation | [
"Hang Zhang",
"Han Zhang",
"Chenguang Wang",
"Junyuan Xie"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Co-Occurrent_Features_in_Semantic_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Co-Occurrent_Features_in_Semantic_Segmentation_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Zhang_2019_CVPR,author = {Zhang, Hang and Zhang, Han and Wang, Chenguang and Xie, Junyuan},title = {Co-Occurrent Features in Semantic Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Recent work has achieved great success in utilizing global contextual information for semantic segmentation, including increasing the receptive field and aggregating pyramid feature representations. In this paper, we go beyond global context and explore the fine-grained representation using co-occurrent features by int... | [
-0.009331746958196163,
-0.01164631824940443,
0.007247890345752239,
0.03400454297661781,
0.030106395483016968,
0.036550987511873245,
0.020992564037442207,
0.034041643142700195,
-0.02250511571764946,
-0.04225609451532364,
-0.046849288046360016,
-0.04060616344213486,
-0.03159328177571297,
0.0... |
56 | Bag of Tricks for Image Classification with Convolutional Neural Networks | [
"Tong He",
"Zhi Zhang",
"Hang Zhang",
"Zhongyue Zhang",
"Junyuan Xie",
"Mu Li"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/He_Bag_of_Tricks_for_Image_Classification_with_Convolutional_Neural_Networks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/He_Bag_of_Tricks_for_Image_Classification_with_Convolutional_Neural_Networks_CVPR_2019_paper.pdf | null | 1812.01187 | title_snapshot | @InProceedings{He_2019_CVPR,author = {He, Tong and Zhang, Zhi and Zhang, Hang and Zhang, Zhongyue and Xie, Junyuan and Li, Mu},title = {Bag of Tricks for Image Classification with Convolutional Neural Networks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month ... | Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In thi... | [
0.018153976649045944,
-0.03299740329384804,
-0.027918823063373566,
0.02573826164007187,
0.021144399419426918,
0.0017883088439702988,
0.011689899489283562,
0.005612026900053024,
-0.005558272823691368,
-0.04303722083568573,
-0.0159681998193264,
0.01044389232993126,
-0.05990973487496376,
0.00... |
57 | Learning Channel-Wise Interactions for Binary Convolutional Neural Networks | [
"Ziwei Wang",
"Jiwen Lu",
"Chenxin Tao",
"Jie Zhou",
"Qi Tian"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Learning_Channel-Wise_Interactions_for_Binary_Convolutional_Neural_Networks_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Learning_Channel-Wise_Interactions_for_Binary_Convolutional_Neural_Networks_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Wang_Learning_Channel-Wise_Interactions_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Wang_2019_CVPR,author = {Wang, Ziwei and Lu, Jiwen and Tao, Chenxin and Zhou, Jie and Tian, Qi},title = {Learning Channel-Wise Interactions for Binary Convolutional Neural Networks},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year ... | In this paper, we propose a channel-wise interaction based binary convolutional neural network learning method (CI-BCNN) for efficient inference. Conventional methods apply xnor and bitcount operations in binary convolution with notable quantization error, which usually obtains inconsistent signs in binary feature maps... | [
0.00357059552334249,
-0.037037089467048645,
-0.003629417158663273,
0.030700650066137314,
0.04998483508825302,
0.024813756346702576,
-0.003252104390412569,
0.005268095526844263,
0.013674403540790081,
-0.02788734808564186,
-0.017881663516163826,
0.024368932470679283,
-0.08027394115924835,
0.... |
58 | Knowledge Adaptation for Efficient Semantic Segmentation | [
"Tong He",
"Chunhua Shen",
"Zhi Tian",
"Dong Gong",
"Changming Sun",
"Youliang Yan"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/He_Knowledge_Adaptation_for_Efficient_Semantic_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/He_Knowledge_Adaptation_for_Efficient_Semantic_Segmentation_CVPR_2019_paper.pdf | null | 1903.04688 | title_snapshot | @InProceedings{He_2019_CVPR,author = {He, Tong and Shen, Chunhua and Tian, Zhi and Gong, Dong and Sun, Changming and Yan, Youliang},title = {Knowledge Adaptation for Efficient Semantic Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},yea... | Both accuracy and efficiency are of significant importance to the task of semantic segmentation. Existing deep FCNs suffer from heavy computations due to a series of high-resolution feature maps for preserving the detailed knowledge in dense estimation. Although reducing the feature map resolution (i.e., applying a lar... | [
0.01294485479593277,
-0.04463597387075424,
0.00826549343764782,
0.04406042397022247,
0.03912551328539848,
0.03910219296813011,
0.024981290102005005,
0.003433498553931713,
-0.013105440884828568,
-0.015172825194895267,
-0.05415186658501625,
-0.010311133228242397,
-0.04418273642659187,
0.0103... |
59 | Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack | [
"Zhezhi He",
"Adnan Siraj Rakin",
"Deliang Fan"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/He_Parametric_Noise_Injection_Trainable_Randomness_to_Improve_Deep_Neural_Network_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/He_Parametric_Noise_Injection_Trainable_Randomness_to_Improve_Deep_Neural_Network_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/He_Parametric_Noise_Injection_CVPR_2019_supplemental.pdf | 1811.09310 | title_snapshot | @InProceedings{He_2019_CVPR,author = {He, Zhezhi and Rakin, Adnan Siraj and Fan, Deliang},title = {Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Recent developments in the field of Deep Learning have exposed the underlying vulnerability of Deep Neural Network (DNN) against adversarial examples. In image classification, an adversarial example is a carefully modified image that is visually imperceptible to the original image but can cause DNN model to misclassify... | [
0.0008452502079308033,
-0.015206334181129932,
-0.03038107231259346,
0.04613994434475899,
0.04009415954351425,
0.061883594840765,
0.04837270453572273,
-0.03335791453719139,
-0.009753648191690445,
-0.06263542920351028,
0.011577136814594269,
-0.040948882699012756,
-0.0465632826089859,
-0.0005... |
60 | Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification | [
"Zhun Zhong",
"Liang Zheng",
"Zhiming Luo",
"Shaozi Li",
"Yi Yang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhong_Invariance_Matters_Exemplar_Memory_for_Domain_Adaptive_Person_Re-Identification_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhong_Invariance_Matters_Exemplar_Memory_for_Domain_Adaptive_Person_Re-Identification_CVPR_2019_paper.pdf | null | 1904.01990 | title_snapshot | @InProceedings{Zhong_2019_CVPR,author = {Zhong, Zhun and Zheng, Liang and Luo, Zhiming and Li, Shaozi and Yang, Yi},title = {Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {J... | This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional methods are mainly to reduce feature distribution gap between the source and target domains. However, these studies largely neglect the intr... | [
-0.006757757160812616,
-0.011635848321020603,
0.01683608628809452,
0.03809748589992523,
0.05577916279435158,
0.017461614683270454,
0.016979049891233444,
-0.011539433151483536,
-0.030057653784751892,
-0.03617282584309578,
-0.03500575199723244,
0.0045452481135725975,
-0.08574368804693222,
-0... |
61 | Dissecting Person Re-Identification From the Viewpoint of Viewpoint | [
"Xiaoxiao Sun",
"Liang Zheng"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Dissecting_Person_Re-Identification_From_the_Viewpoint_of_Viewpoint_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Dissecting_Person_Re-Identification_From_the_Viewpoint_of_Viewpoint_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Sun_Dissecting_Person_Re-Identification_CVPR_2019_supplemental.pdf | 1812.02162 | title_snapshot | @InProceedings{Sun_2019_CVPR,author = {Sun, Xiaoxiao and Zheng, Liang},title = {Dissecting Person Re-Identification From the Viewpoint of Viewpoint},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Variations in visual factors such as viewpoint, pose, illumination and background, are usually viewed as important challenges in person re-identification (re-ID). In spite of acknowledging these factors to be influential, quantitative studies on how they affect a re-ID system are still lacking. To derive insights in th... | [
0.015039617195725441,
-0.028424249961972237,
-0.006867963820695877,
0.040380362421274185,
0.03754907846450806,
0.010733168572187424,
0.015423051081597805,
0.0073539502918720245,
-0.033290885388851166,
-0.04221372306346893,
-0.05590500682592392,
-0.01778329722583294,
-0.09187813848257065,
-... |
62 | Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification | [
"Zhixiang Wang",
"Zheng Wang",
"Yinqiang Zheng",
"Yung-Yu Chuang",
"Shin'ichi Satoh"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Learning_to_Reduce_Dual-Level_Discrepancy_for_Infrared-Visible_Person_Re-Identification_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Learning_to_Reduce_Dual-Level_Discrepancy_for_Infrared-Visible_Person_Re-Identification_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Wang_2019_CVPR,author = {Wang, Zhixiang and Wang, Zheng and Zheng, Yinqiang and Chuang, Yung-Yu and Satoh, Shin'ichi},title = {Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Infrared-Visible person RE-IDentification (IV-REID) is a rising task. Compared to conventional person re-identification (re-ID), IV-REID concerns the additional modality discrepancy originated from the different imaging processes of spectrum cameras, in addition to the person's appearance discrepancy caused by viewpoin... | [
0.003764213528484106,
-0.00001636260458326433,
0.006607317831367254,
0.03092047944664955,
0.04836612567305565,
0.009268427267670631,
0.05379283428192139,
-0.015492348000407219,
-0.0372406542301178,
-0.07102212309837341,
-0.03527715429663658,
0.013884318061172962,
-0.0751379206776619,
0.002... |
63 | Progressive Feature Alignment for Unsupervised Domain Adaptation | [
"Chaoqi Chen",
"Weiping Xie",
"Wenbing Huang",
"Yu Rong",
"Xinghao Ding",
"Yue Huang",
"Tingyang Xu",
"Junzhou Huang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Progressive_Feature_Alignment_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Progressive_Feature_Alignment_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Chen_Progressive_Feature_Alignment_CVPR_2019_supplemental.pdf | 1811.08585 | title_snapshot | @InProceedings{Chen_2019_CVPR,author = {Chen, Chaoqi and Xie, Weiping and Huang, Wenbing and Rong, Yu and Ding, Xinghao and Huang, Yue and Xu, Tingyang and Huang, Junzhou},title = {Progressive Feature Alignment for Unsupervised Domain Adaptation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains.... | [
0.0033854187931865454,
-0.00871092826128006,
-0.008151226677000523,
0.03311604633927345,
0.05267776548862457,
0.02708711475133896,
0.05289147049188614,
-0.0157597828656435,
0.0251811183989048,
-0.030351020395755768,
-0.021104108542203903,
0.000563057663384825,
-0.08134517073631287,
0.02794... |
64 | Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition | [
"Xiaofeng Liu",
"Site Li",
"Lingsheng Kong",
"Wanqing Xie",
"Ping Jia",
"Jane You",
"B.V.K. Kumar"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Feature-Level_Frankenstein_Eliminating_Variations_for_Discriminative_Recognition_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Feature-Level_Frankenstein_Eliminating_Variations_for_Discriminative_Recognition_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Liu_2019_CVPR,author = {Liu, Xiaofeng and Li, Site and Kong, Lingsheng and Xie, Wanqing and Jia, Ping and You, Jane and Kumar, B.V.K.},title = {Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | Recent successes of deep learning-based recognition rely on maintaining the content related to the main-task label. However, how to explicitly dispel the noisy signals for better generalization remains an open issue. We systematically summarize the detrimental factors as task-relevant/irrelevant semantic variations and... | [
-0.0139079000800848,
-0.006796400994062424,
-0.012729011476039886,
0.04368659108877182,
0.027704879641532898,
0.042876943945884705,
0.03199850022792816,
-0.013829716481268406,
-0.02073376625776291,
-0.0444224551320076,
-0.03149246424436569,
0.013633035123348236,
-0.07698412239551544,
0.004... |
65 | Learning a Deep ConvNet for Multi-Label Classification With Partial Labels | [
"Thibaut Durand",
"Nazanin Mehrasa",
"Greg Mori"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Durand_Learning_a_Deep_ConvNet_for_Multi-Label_Classification_With_Partial_Labels_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Durand_Learning_a_Deep_ConvNet_for_Multi-Label_Classification_With_Partial_Labels_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Durand_Learning_a_Deep_CVPR_2019_supplemental.pdf | 1902.09720 | title_snapshot | @InProceedings{Durand_2019_CVPR,author = {Durand, Thibaut and Mehrasa, Nazanin and Mori, Greg},title = {Learning a Deep ConvNet for Multi-Label Classification With Partial Labels},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multi-label. Multi-label classification is a more difficult task than single-label classification bec... | [
0.006116017699241638,
-0.05545556917786598,
-0.045087698847055435,
0.05700509250164032,
0.025540804490447044,
0.02632475644350052,
-0.0009101619943976402,
-0.004233519081026316,
-0.010545865632593632,
-0.03018064983189106,
-0.00879135262221098,
0.017379585653543472,
-0.059217147529125214,
... |
66 | Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression | [
"Hamid Rezatofighi",
"Nathan Tsoi",
"JunYoung Gwak",
"Amir Sadeghian",
"Ian Reid",
"Silvio Savarese"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Rezatofighi_Generalized_Intersection_Over_Union_A_Metric_and_a_Loss_for_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Rezatofighi_Generalized_Intersection_Over_Union_A_Metric_and_a_Loss_for_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Rezatofighi_Generalized_Intersection_Over_CVPR_2019_supplemental.pdf | 1902.09630 | title_snapshot | @InProceedings{Rezatofighi_2019_CVPR,author = {Rezatofighi, Hamid and Tsoi, Nathan and Gwak, JunYoung and Sadeghian, Amir and Reid, Ian and Savarese, Silvio},title = {Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric its... | [
-0.008645880036056042,
-0.0011613622773438692,
0.0038991072215139866,
-0.0016342573799192905,
0.0026631595101207495,
0.04965602606534958,
0.021770959720015526,
0.019214756786823273,
-0.015602534636855125,
-0.04125526547431946,
-0.04100135341286659,
-0.0001416221057297662,
-0.1009972095489502... |
67 | Densely Semantically Aligned Person Re-Identification | [
"Zhizheng Zhang",
"Cuiling Lan",
"Wenjun Zeng",
"Zhibo Chen"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Densely_Semantically_Aligned_Person_Re-Identification_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Densely_Semantically_Aligned_Person_Re-Identification_CVPR_2019_paper.pdf | null | 1812.08967 | title_snapshot | @InProceedings{Zhang_2019_CVPR,author = {Zhang, Zhizheng and Lan, Cuiling and Zeng, Wenjun and Chen, Zhibo},title = {Densely Semantically Aligned Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | We propose a densely semantically aligned person re-identification (re-ID) framework. It fundamentally addresses the body misalignment problem caused by pose/viewpoint variations, imperfect person detection, occlusion, etc.. By leveraging the estimation of the dense semantics of a person image, we construct a set of de... | [
0.026053402572870255,
-0.050012387335300446,
0.009049152955412865,
0.05683193355798721,
0.02772638201713562,
0.0180633794516325,
-0.003440648317337036,
-0.027285439893603325,
-0.03275596350431442,
-0.04798392578959465,
-0.047841329127550125,
-0.03310380131006241,
-0.0926205962896347,
-0.00... |
68 | Generalising Fine-Grained Sketch-Based Image Retrieval | [
"Kaiyue Pang",
"Ke Li",
"Yongxin Yang",
"Honggang Zhang",
"Timothy M. Hospedales",
"Tao Xiang",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Pang_Generalising_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Pang_Generalising_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Pang_2019_CVPR,author = {Pang, Kaiyue and Li, Ke and Yang, Yongxin and Zhang, Honggang and Hospedales, Timothy M. and Xiang, Tao and Song, Yi-Zhe},title = {Generalising Fine-Grained Sketch-Based Image Retrieval},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Fine-grained sketch-based image retrieval (FG-SBIR) addresses matching specific photo instance using free-hand sketch as a query modality. Existing models aim to learn an embedding space in which sketch and photo can be directly compared. While successful, they require instance-level pairing within each coarse-grained ... | [
-0.005855536554008722,
-0.046595629304647446,
0.03436077758669853,
0.038160670548677444,
0.034400325268507004,
0.013324089348316193,
0.01764552667737007,
0.010514887049794197,
-0.025579553097486496,
-0.05550779774785042,
-0.03578289598226547,
-0.013509687036275864,
-0.08774726092815399,
-0... |
69 | Adapting Object Detectors via Selective Cross-Domain Alignment | [
"Xinge Zhu",
"Jiangmiao Pang",
"Ceyuan Yang",
"Jianping Shi",
"Dahua Lin"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Adapting_Object_Detectors_via_Selective_Cross-Domain_Alignment_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Adapting_Object_Detectors_via_Selective_Cross-Domain_Alignment_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Zhu_2019_CVPR,author = {Zhu, Xinge and Pang, Jiangmiao and Yang, Ceyuan and Shi, Jianping and Lin, Dahua},title = {Adapting Object Detectors via Selective Cross-Domain Alignment},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {... | State-of-the-art object detectors are usually trained on public datasets. They often face substantial difficulties when applied to a different domain, where the imaging condition differs significantly and the corresponding annotated data are unavailable (or expensive to acquire). A natural remedy is to adapt the model ... | [
-0.0019574915058910847,
-0.004494640044867992,
0.0015389469917863607,
0.0302722305059433,
0.023903759196400642,
0.023777376860380173,
0.016474522650241852,
-0.005437968298792839,
-0.034574344754219055,
-0.054253824055194855,
-0.045855019241571426,
0.01820206828415394,
-0.08005546778440475,
... |
70 | Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation | [
"Yunhang Shen",
"Rongrong Ji",
"Yan Wang",
"Yongjian Wu",
"Liujuan Cao"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Shen_2019_CVPR,author = {Shen, Yunhang and Ji, Rongrong and Wang, Yan and Wu, Yongjian and Cao, Liujuan},title = {Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Weakly supervised learning has attracted growing research attention due to the significant saving in annotation cost for tasks that require intra-image annotations, such as object detection and semantic segmentation. To this end, existing weakly supervised object detection and semantic segmentation approaches follow an... | [
0.014649857766926289,
-0.015168294310569763,
-0.022458717226982117,
0.01517539843916893,
0.006244993768632412,
0.016321348026394844,
0.028863929212093353,
0.0028987890109419823,
-0.024212243035435677,
-0.037388235330581665,
-0.05070701241493225,
0.0162052009254694,
-0.03748846426606178,
-0... |
71 | Thinking Outside the Pool: Active Training Image Creation for Relative Attributes | [
"Aron Yu",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Thinking_Outside_the_Pool_Active_Training_Image_Creation_for_Relative_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Thinking_Outside_the_Pool_Active_Training_Image_Creation_for_Relative_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Yu_Thinking_Outside_the_CVPR_2019_supplemental.pdf | 1901.02551 | title_snapshot | @InProceedings{Yu_2019_CVPR,author = {Yu, Aron and Grauman, Kristen},title = {Thinking Outside the Pool: Active Training Image Creation for Relative Attributes},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is problematic for fine-grained attributes, where the subtle visual differences of in... | [
0.023849915713071823,
-0.03497087582945824,
-0.011310216039419174,
0.06219130754470825,
0.020453788340091705,
0.008081084117293358,
-0.009885863400995731,
0.0029116494115442038,
-0.023094989359378815,
-0.053703974932432175,
-0.050772905349731445,
0.007894223555922508,
-0.06742726266384125,
... |
72 | Generalizable Person Re-Identification by Domain-Invariant Mapping Network | [
"Jifei Song",
"Yongxin Yang",
"Yi-Zhe Song",
"Tao Xiang",
"Timothy M. Hospedales"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Song_Generalizable_Person_Re-Identification_by_Domain-Invariant_Mapping_Network_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Song_Generalizable_Person_Re-Identification_by_Domain-Invariant_Mapping_Network_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Song_2019_CVPR,author = {Song, Jifei and Yang, Yongxin and Song, Yi-Zhe and Xiang, Tao and Hospedales, Timothy M.},title = {Generalizable Person Re-Identification by Domain-Invariant Mapping Network},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},mo... | We aim to learn a domain generalizable person re-identification (ReID) model. When such a model is trained on a set of source domains (ReID datasets collected from different camera networks), it can be directly applied to any new unseen dataset for effective ReID without any model updating. Despite its practical value ... | [
0.0018666652031242847,
-0.03594958782196045,
0.012643176130950451,
0.03442191705107689,
0.034795332700014114,
0.013469260185956955,
0.0550093874335289,
-0.01416036207228899,
-0.030186451971530914,
-0.03378211706876755,
-0.036294061690568924,
-0.01064358465373516,
-0.10261493921279907,
-0.0... |
73 | Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification | [
"Hao Guo",
"Kang Zheng",
"Xiaochuan Fan",
"Hongkai Yu",
"Song Wang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Guo_Visual_Attention_Consistency_Under_Image_Transforms_for_Multi-Label_Image_Classification_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Guo_Visual_Attention_Consistency_Under_Image_Transforms_for_Multi-Label_Image_Classification_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Guo_Visual_Attention_Consistency_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Guo_2019_CVPR,author = {Guo, Hao and Zheng, Kang and Fan, Xiaochuan and Yu, Hongkai and Wang, Song},title = {Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},mon... | Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are included for training b... | [
0.01757747493684292,
-0.025658247992396355,
-0.02381902188062668,
0.04838621988892555,
0.040749259293079376,
0.03908943757414818,
0.015500809997320175,
0.017121227458119392,
-0.04253159463405609,
-0.049758803099393845,
-0.03973410651087761,
0.0018427345203235745,
-0.07539146393537521,
0.00... |
74 | Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification | [
"Song Bai",
"Peng Tang",
"Philip H.S. Torr",
"Longin Jan Latecki"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Bai_Re-Ranking_via_Metric_Fusion_for_Object_Retrieval_and_Person_Re-Identification_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Bai_Re-Ranking_via_Metric_Fusion_for_Object_Retrieval_and_Person_Re-Identification_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Bai_Re-Ranking_via_Metric_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Bai_2019_CVPR,author = {Bai, Song and Tang, Peng and Torr, Philip H.S. and Latecki, Longin Jan},title = {Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | This work studies the unsupervised re-ranking procedure for object retrieval and person re-identification with a specific concentration on an ensemble of multiple metrics (or similarities). While the re-ranking step is involved by running a diffusion process on the underlying data manifolds, the fusion step can leverag... | [
-0.02814074046909809,
-0.06253652274608612,
0.005154592450708151,
0.05284074321389198,
0.041109614074230194,
0.013369444757699966,
-0.008197794668376446,
-0.00558644812554121,
-0.0282584335654974,
-0.06852797418832779,
-0.026356063783168793,
-0.020197197794914246,
-0.07970239222049713,
-0.... |
75 | Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization | [
"Junbao Zhuo",
"Shuhui Wang",
"Shuhao Cui",
"Qingming Huang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhuo_Unsupervised_Open_Domain_Recognition_by_Semantic_Discrepancy_Minimization_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhuo_Unsupervised_Open_Domain_Recognition_by_Semantic_Discrepancy_Minimization_CVPR_2019_paper.pdf | null | 1904.08631 | title_snapshot | @InProceedings{Zhuo_2019_CVPR,author = {Zhuo, Junbao and Wang, Shuhui and Cui, Shuhao and Huang, Qingming},title = {Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {201... | We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is to correctly classify all samples in T including known and unknown categories. UODR is challenging due to the domain discrepancy, which beco... | [
-0.00948365218937397,
-0.023822614923119545,
-0.004167474340647459,
0.04614853858947754,
0.03375883400440216,
-0.007113227620720863,
0.010893025435507298,
0.006613012868911028,
-0.0054511516354978085,
-0.02079329825937748,
-0.015183866024017334,
0.01659412682056427,
-0.0844886377453804,
0.... |
76 | Weakly Supervised Person Re-Identification | [
"Jingke Meng",
"Sheng Wu",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Meng_Weakly_Supervised_Person_Re-Identification_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Meng_Weakly_Supervised_Person_Re-Identification_CVPR_2019_paper.pdf | null | 1904.03832 | title_snapshot | @InProceedings{Meng_2019_CVPR,author = {Meng, Jingke and Wu, Sheng and Zheng, Wei-Shi},title = {Weakly Supervised Person Re-Identification},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual; this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervise... | [
0.009438742883503437,
-0.03998570889234543,
-0.005069916136562824,
0.04764261841773987,
0.036476440727710724,
0.006063239648938179,
0.008812325075268745,
-0.01201524119824171,
-0.022716961801052094,
-0.02688215672969818,
-0.017068754881620407,
0.0029347476083785295,
-0.10724011808633804,
0... |
77 | PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud | [
"Shaoshuai Shi",
"Xiaogang Wang",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Shi_PointRCNN_3D_Object_Proposal_Generation_and_Detection_From_Point_Cloud_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_PointRCNN_3D_Object_Proposal_Generation_and_Detection_From_Point_Cloud_CVPR_2019_paper.pdf | null | 1812.04244 | title_snapshot | @InProceedings{Shi_2019_CVPR,author = {Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng},title = {PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RG... | [
0.01849064789712429,
0.003913921769708395,
0.011638566851615906,
0.04790153354406357,
0.01876726560294628,
0.046270109713077545,
-0.02690240554511547,
0.006130681373178959,
-0.03689471259713173,
-0.038423292338848114,
-0.05275751277804375,
-0.035745441913604736,
-0.048396527767181396,
0.00... |
78 | Automatic Adaptation of Object Detectors to New Domains Using Self-Training | [
"Aruni RoyChowdhury",
"Prithvijit Chakrabarty",
"Ashish Singh",
"SouYoung Jin",
"Huaizu Jiang",
"Liangliang Cao",
"Erik Learned-Miller"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/RoyChowdhury_Automatic_Adaptation_of_Object_Detectors_to_New_Domains_Using_Self-Training_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/RoyChowdhury_Automatic_Adaptation_of_Object_Detectors_to_New_Domains_Using_Self-Training_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/RoyChowdhury_Automatic_Adaptation_of_CVPR_2019_supplemental.pdf | 1904.07305 | title_snapshot | @InProceedings{RoyChowdhury_2019_CVPR,author = {RoyChowdhury, Aruni and Chakrabarty, Prithvijit and Singh, Ashish and Jin, SouYoung and Jiang, Huaizu and Cao, Liangliang and Learned-Miller, Erik},title = {Automatic Adaptation of Object Detectors to New Domains Using Self-Training},booktitle = {Proceedings of the IEEE/C... | This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readily available. We automatically obtain labels on the target data by using high-confidence detections from the existing detector, augmented wit... | [
0.006639634259045124,
-0.016986681148409843,
0.010191033594310284,
0.040521856397390366,
0.04244900122284889,
0.0017049264861270785,
0.028737984597682953,
-0.012430583126842976,
-0.020518803969025612,
-0.02445625141263008,
-0.059264786541461945,
0.025007005780935287,
-0.07432584464550018,
... |
79 | Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing | [
"Jiaxin Chen",
"Jie Qin",
"Li Liu",
"Fan Zhu",
"Fumin Shen",
"Jin Xie",
"Ling Shao"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Chen_2019_CVPR,author = {Chen, Jiaxin and Qin, Jie and Liu, Li and Zhu, Fan and Shen, Fumin and Xie, Jin and Shao, Ling},title = {Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | Sketch-based 3D shape retrieval has been extensively studied in recent works, most of which focus on improving the retrieval accuracy, whilst neglecting the efficiency. In this paper, we propose a novel framework for efficient sketch-based 3D shape retrieval, i.e., Deep Sketch-Shape Hashing (DSSH), which tackles the ch... | [
0.007485136389732361,
-0.008028262294828892,
-0.013253485783934593,
0.04939042776823044,
0.028057731688022614,
0.043675292283296585,
0.01368318498134613,
0.0014264212222769856,
-0.02768218331038952,
-0.08734215050935745,
-0.017215080559253693,
-0.04802378639578819,
-0.05540759488940239,
0.... |
80 | Generative Dual Adversarial Network for Generalized Zero-Shot Learning | [
"He Huang",
"Changhu Wang",
"Philip S. Yu",
"Chang-Dong Wang"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Huang_Generative_Dual_Adversarial_Network_for_Generalized_Zero-Shot_Learning_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Huang_Generative_Dual_Adversarial_Network_for_Generalized_Zero-Shot_Learning_CVPR_2019_paper.pdf | null | 1811.04857 | title_snapshot | @InProceedings{Huang_2019_CVPR,author = {Huang, He and Wang, Changhu and Yu, Philip S. and Wang, Chang-Dong},title = {Generative Dual Adversarial Network for Generalized Zero-Shot Learning},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019... | This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. In this paper, we propose a novel model that provides a unified framework for three different ... | [
0.01859661191701889,
-0.029138248413801193,
-0.013606123626232147,
0.046068064868450165,
0.027556201443076134,
0.0380835086107254,
0.01571362279355526,
0.024943118914961815,
-0.017352264374494553,
-0.035500772297382355,
-0.03787927329540253,
0.003221124177798629,
-0.07168801128864288,
0.01... |
81 | Query-Guided End-To-End Person Search | [
"Bharti Munjal",
"Sikandar Amin",
"Federico Tombari",
"Fabio Galasso"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.pdf | null | 1905.01203 | title_snapshot | @InProceedings{Munjal_2019_CVPR,author = {Munjal, Bharti and Amin, Sikandar and Tombari, Federico and Galasso, Fabio},title = {Query-Guided End-To-End Person Search},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii.... | [
-0.006783336400985718,
-0.04385999217629433,
0.0012134795542806387,
0.03677099570631981,
0.024804910644888878,
-0.017758768051862717,
0.013924302533268929,
-0.010790892876684666,
-0.03499196097254753,
-0.03876211866736412,
-0.08296401798725128,
-0.018198609352111816,
-0.07606885582208633,
... |
82 | Libra R-CNN: Towards Balanced Learning for Object Detection | [
"Jiangmiao Pang",
"Kai Chen",
"Jianping Shi",
"Huajun Feng",
"Wanli Ouyang",
"Dahua Lin"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Pang_Libra_R-CNN_Towards_Balanced_Learning_for_Object_Detection_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Pang_Libra_R-CNN_Towards_Balanced_Learning_for_Object_Detection_CVPR_2019_paper.pdf | null | 1904.02701 | title_snapshot | @InProceedings{Pang_2019_CVPR,author = {Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Lin, Dahua},title = {Libra R-CNN: Towards Balanced Learning for Object Detection},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = ... | Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalanc... | [
0.03636639192700386,
-0.01169802900403738,
0.03305500000715256,
-0.011125626042485237,
0.0362614206969738,
0.03133784979581833,
0.000013725018106924836,
-0.005247221793979406,
-0.028941625729203224,
-0.04928097501397133,
-0.021287426352500916,
0.00016316484834533185,
-0.07220053672790527,
... |
83 | Learning a Unified Classifier Incrementally via Rebalancing | [
"Saihui Hou",
"Xinyu Pan",
"Chen Change Loy",
"Zilei Wang",
"Dahua Lin"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Hou_Learning_a_Unified_Classifier_Incrementally_via_Rebalancing_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Hou_Learning_a_Unified_Classifier_Incrementally_via_Rebalancing_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Hou_Learning_a_Unified_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Hou_2019_CVPR,author = {Hou, Saihui and Pan, Xinyu and Loy, Chen Change and Wang, Zilei and Lin, Dahua},title = {Learning a Unified Classifier Incrementally via Rebalancing},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}... | Conventionally, deep neural networks are trained offline, relying on a large dataset prepared in advance. This paradigm is often challenged in real-world applications, e.g. online services that involve continuous streams of incoming data. Recently, incremental learning receives increasing attention, and is considered a... | [
-0.0027312072925269604,
-0.060094743967056274,
-0.002515501342713833,
0.015660738572478294,
0.025659523904323578,
0.02684149332344532,
0.010970444418489933,
0.013750061392784119,
-0.020341971889138222,
-0.040708277374506,
0.011086929589509964,
-0.019031904637813568,
-0.0836915671825409,
0.... |
84 | Feature Selective Anchor-Free Module for Single-Shot Object Detection | [
"Chenchen Zhu",
"Yihui He",
"Marios Savvides"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Feature_Selective_Anchor-Free_Module_for_Single-Shot_Object_Detection_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Feature_Selective_Anchor-Free_Module_for_Single-Shot_Object_Detection_CVPR_2019_paper.pdf | null | 1903.00621 | title_snapshot | @InProceedings{Zhu_2019_CVPR,author = {Zhu, Chenchen and He, Yihui and Savvides, Marios},title = {Feature Selective Anchor-Free Module for Single-Shot Object Detection},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) he... | [
-0.0014583100564777851,
0.008192913606762886,
0.03194105997681618,
0.013472149148583412,
0.05590756982564926,
0.027777669951319695,
0.01450942549854517,
0.01127221342176199,
-0.015436569228768349,
-0.04577988386154175,
-0.017727717757225037,
0.01056344248354435,
-0.09335751831531525,
-0.01... |
85 | Bottom-Up Object Detection by Grouping Extreme and Center Points | [
"Xingyi Zhou",
"Jiacheng Zhuo",
"Philipp Krahenbuhl"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhou_Bottom-Up_Object_Detection_by_Grouping_Extreme_and_Center_Points_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhou_Bottom-Up_Object_Detection_by_Grouping_Extreme_and_Center_Points_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Zhou_Bottom-Up_Object_Detection_CVPR_2019_supplemental.pdf | 1901.08043 | title_snapshot | @InProceedings{Zhou_2019_CVPR,author = {Zhou, Xingyi and Zhuo, Jiacheng and Krahenbuhl, Philipp},title = {Bottom-Up Object Detection by Grouping Extreme and Center Points},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem. State of the art algorithms enumerate a near-exhaustive list of object locations and classify each into: object or not. In this paper, we show that bottom-up approaches still perform competitively. We detect f... | [
-0.04537883400917053,
0.004217203706502914,
0.029943889006972313,
0.01207615528255701,
0.01966908946633339,
0.02028551697731018,
0.0036954740062355995,
0.006305204704403877,
-0.037432510405778885,
-0.038860779255628586,
-0.04019107669591904,
-0.018623942509293556,
-0.05674735829234123,
-0.... |
86 | Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples | [
"Zihao Liu",
"Qi Liu",
"Tao Liu",
"Nuo Xu",
"Xue Lin",
"Yanzhi Wang",
"Wujie Wen"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Feature_Distillation_DNN-Oriented_JPEG_Compression_Against_Adversarial_Examples_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Feature_Distillation_DNN-Oriented_JPEG_Compression_Against_Adversarial_Examples_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Liu_Feature_Distillation_DNN-Oriented_CVPR_2019_supplemental.pdf | 1803.05787 | title_snapshot | @InProceedings{Liu_2019_CVPR,author = {Liu, Zihao and Liu, Qi and Liu, Tao and Xu, Nuo and Lin, Xue and Wang, Yanzhi and Wen, Wujie},title = {Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently. However, prior works mainly rely on directly tuning parameters like compression rate, to blindly reduce image features, thereby lacking ... | [
-0.001302432152442634,
-0.018670307472348213,
-0.03043823502957821,
0.05203220248222351,
0.05232861638069153,
0.04078221321105957,
0.03127900883555412,
-0.032759372144937515,
-0.038749344646930695,
-0.03963088616728783,
-0.022269725799560547,
-0.04257231205701828,
-0.053149253129959106,
0.... |
87 | SCOPS: Self-Supervised Co-Part Segmentation | [
"Wei-Chih Hung",
"Varun Jampani",
"Sifei Liu",
"Pavlo Molchanov",
"Ming-Hsuan Yang",
"Jan Kautz"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Hung_SCOPS_Self-Supervised_Co-Part_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Hung_SCOPS_Self-Supervised_Co-Part_Segmentation_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Hung_SCOPS_Self-Supervised_Co-Part_CVPR_2019_supplemental.pdf | 1905.01298 | title_snapshot | @InProceedings{Hung_2019_CVPR,author = {Hung, Wei-Chih and Jampani, Varun and Liu, Sifei and Molchanov, Pavlo and Yang, Ming-Hsuan and Kautz, Jan},title = {SCOPS: Self-Supervised Co-Part Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},y... | Parts provide a good intermediate representation of objects that is robust with respect to camera, pose and appearance variations. Existing work on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and also can not generalize to unseen object categories. We propose... | [
-0.0023917974904179573,
-0.031977418810129166,
-0.02159789390861988,
0.050480179488658905,
0.02908567152917385,
0.043632011860609055,
0.0010789762018248439,
0.014653769321739674,
-0.028405694290995598,
-0.028022268787026405,
-0.05794256925582886,
-0.0225776806473732,
-0.06358082592487335,
... |
88 | Unsupervised Moving Object Detection via Contextual Information Separation | [
"Yanchao Yang",
"Antonio Loquercio",
"Davide Scaramuzza",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Unsupervised_Moving_Object_Detection_via_Contextual_Information_Separation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Unsupervised_Moving_Object_Detection_via_Contextual_Information_Separation_CVPR_2019_paper.pdf | null | 1901.03360 | title_snapshot | @InProceedings{Yang_2019_CVPR,author = {Yang, Yanchao and Loquercio, Antonio and Scaramuzza, Davide and Soatto, Stefano},title = {Unsupervised Moving Object Detection via Contextual Information Separation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {Ju... | We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible. The result is a ... | [
0.008167168125510216,
-0.00787293165922165,
-0.00002057650090137031,
0.04392854496836662,
0.015141691081225872,
0.016596786677837372,
-0.0007000970072112978,
0.01526574231684208,
-0.06357830762863159,
-0.04312210530042648,
-0.06515786796808243,
0.014768091030418873,
-0.05057407543063164,
0... |
89 | Pose2Seg: Detection Free Human Instance Segmentation | [
"Song-Hai Zhang",
"Ruilong Li",
"Xin Dong",
"Paul Rosin",
"Zixi Cai",
"Xi Han",
"Dingcheng Yang",
"Haozhi Huang",
"Shi-Min Hu"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Pose2Seg_Detection_Free_Human_Instance_Segmentation_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Pose2Seg_Detection_Free_Human_Instance_Segmentation_CVPR_2019_paper.pdf | null | 1803.10683 | title_snapshot | @InProceedings{Zhang_2019_CVPR,author = {Zhang, Song-Hai and Li, Ruilong and Dong, Xin and Rosin, Paul and Cai, Zixi and Han, Xi and Yang, Dingcheng and Huang, Haozhi and Hu, Shi-Min},title = {Pose2Seg: Detection Free Human Instance Segmentation},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN perform them jointly. However, little research takes into account the uniqueness of the "human" category, which... | [
-0.0031797438859939575,
-0.02804473415017128,
-0.03142428770661354,
0.026877542957663536,
0.016673214733600616,
0.034141696989536285,
0.01702551171183586,
0.01618424989283085,
-0.04597628861665726,
-0.040295276790857315,
-0.03885268419981003,
-0.03178657218813896,
-0.09564173966646194,
-0.... |
90 | DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios | [
"Guorun Yang",
"Xiao Song",
"Chaoqin Huang",
"Zhidong Deng",
"Jianping Shi",
"Bolei Zhou"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Yang_DrivingStereo_A_Large-Scale_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Yang_2019_CVPR,author = {Yang, Guorun and Song, Xiao and Huang, Chaoqin and Deng, Zhidong and Shi, Jianping and Zhou, Bolei},title = {DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Great progress has been made on estimating disparity maps from stereo images. However, with the limited stereo data available in the existing datasets and unstable ranging precision of current stereo methods, industry-level stereo matching in autonomous driving remains challenging. In this paper, we construct a novel l... | [
0.016456404700875282,
-0.012661470100283623,
-0.006509444210678339,
0.040632348507642746,
0.03308369591832161,
0.05940622836351395,
0.01563393324613571,
0.0106936851516366,
0.0035824391525238752,
-0.039905790239572525,
-0.029194314032793045,
-0.010720972903072834,
-0.0828021913766861,
0.00... |
91 | PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding | [
"Kaichun Mo",
"Shilin Zhu",
"Angel X. Chang",
"Li Yi",
"Subarna Tripathi",
"Leonidas J. Guibas",
"Hao Su"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Mo_PartNet_A_Large-Scale_Benchmark_for_Fine-Grained_and_Hierarchical_Part-Level_3D_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Mo_PartNet_A_Large-Scale_Benchmark_for_Fine-Grained_and_Hierarchical_Part-Level_3D_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Mo_PartNet_A_Large-Scale_CVPR_2019_supplemental.pdf | 1812.02713 | title_snapshot | @InProceedings{Mo_2019_CVPR,author = {Mo, Kaichun and Zhu, Shilin and Chang, Angel X. and Yi, Li and Tripathi, Subarna and Guibas, Leonidas J. and Su, Hao},title = {PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding},booktitle = {Proceedings of the IEEE/CVF Conference ... | We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. Our dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as ... | [
-0.010400582104921341,
-0.010635788552463055,
0.0014398718485608697,
0.034189093858003616,
0.015206372365355492,
0.036024387925863266,
0.007877465337514877,
0.005755167454481125,
-0.025489920750260353,
-0.03729688748717308,
-0.0373004786670208,
-0.026880916208028793,
-0.03872868791222572,
... |
92 | A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing | [
"Shifeng Zhang",
"Xiaobo Wang",
"Ajian Liu",
"Chenxu Zhao",
"Jun Wan",
"Sergio Escalera",
"Hailin Shi",
"Zezheng Wang",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_A_Dataset_and_Benchmark_for_Large-Scale_Multi-Modal_Face_Anti-Spoofing_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_A_Dataset_and_Benchmark_for_Large-Scale_Multi-Modal_Face_Anti-Spoofing_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Zhang_A_Dataset_and_CVPR_2019_supplemental.pdf | 1812.00408 | title_snapshot | @InProceedings{Zhang_2019_CVPR,author = {Zhang, Shifeng and Wang, Xiaobo and Liu, Ajian and Zhao, Chenxu and Wan, Jun and Escalera, Sergio and Shi, Hailin and Wang, Zezheng and Li, Stan Z.},title = {A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing},booktitle = {Proceedings of the IEEE/CVF Conferen... | Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (<=170) and modalities (<=2), wh... | [
0.0029062244575470686,
-0.04069122299551964,
0.022817296907305717,
0.04405880719423294,
0.04291617125272751,
0.0016610195161774755,
0.04554525017738342,
0.0066553764045238495,
-0.02674783580005169,
-0.048408810049295425,
-0.024022970348596573,
0.016318760812282562,
-0.08085515350103378,
-0... |
93 | Unsupervised Learning of Consensus Maximization for 3D Vision Problems | [
"Thomas Probst",
"Danda Pani Paudel",
"Ajad Chhatkuli",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Probst_Unsupervised_Learning_of_Consensus_Maximization_for_3D_Vision_Problems_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Probst_Unsupervised_Learning_of_Consensus_Maximization_for_3D_Vision_Problems_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Probst_Unsupervised_Learning_of_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Probst_2019_CVPR,author = {Probst, Thomas and Paudel, Danda Pani and Chhatkuli, Ajad and Gool, Luc Van},title = {Unsupervised Learning of Consensus Maximization for 3D Vision Problems},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | Consensus maximization is a key strategy in 3D vision for robust geometric model estimation from measurements with outliers. Generic methods for consensus maximization, such as Random Sampling and Consensus (RANSAC), have played a tremendous role in the success of 3D vision, in spite of the ubiquity of outliers. Howeve... | [
0.02223922125995159,
-0.018861912190914154,
0.004829966928809881,
0.025893639773130417,
0.02153509482741356,
0.017808882519602776,
0.01031549833714962,
0.0010894177248701453,
-0.004766012541949749,
-0.05210335925221443,
-0.023640373721718788,
-0.01805698312819004,
-0.08612985163927078,
0.0... |
94 | VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People | [
"Danna Gurari",
"Qing Li",
"Chi Lin",
"Yinan Zhao",
"Anhong Guo",
"Abigale Stangl",
"Jeffrey P. Bigham"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Gurari_VizWiz-Priv_A_Dataset_for_Recognizing_the_Presence_and_Purpose_of_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Gurari_VizWiz-Priv_A_Dataset_for_Recognizing_the_Presence_and_Purpose_of_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Gurari_VizWiz-Priv_A_Dataset_CVPR_2019_supplemental.pdf | null | null | @InProceedings{Gurari_2019_CVPR,author = {Gurari, Danna and Li, Qing and Lin, Chi and Zhao, Yinan and Guo, Anhong and Stangl, Abigale and Bigham, Jeffrey P.},title = {VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People},booktitle = {Proceedings o... | We introduce the first visual privacy dataset originating from people who are blind in order to better understand their privacy disclosures and to encourage the development of algorithms that can assist in preventing their unintended disclosures. It includes 8,862 regions showing private content across 5,537 images ta... | [
0.026420287787914276,
-0.018837206065654755,
0.011302880942821503,
0.043129514902830124,
0.041569437831640244,
0.0009297491051256657,
0.07058495283126831,
0.00942026637494564,
-0.027611810714006424,
-0.05271301418542862,
-0.04992416873574257,
0.01964474841952324,
-0.057631298899650574,
-0.... |
95 | Structural Relational Reasoning of Point Clouds | [
"Yueqi Duan",
"Yu Zheng",
"Jiwen Lu",
"Jie Zhou",
"Qi Tian"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Duan_Structural_Relational_Reasoning_of_Point_Clouds_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Duan_Structural_Relational_Reasoning_of_Point_Clouds_CVPR_2019_paper.pdf | null | null | null | @InProceedings{Duan_2019_CVPR,author = {Duan, Yueqi and Zheng, Yu and Lu, Jiwen and Zhou, Jie and Tian, Qi},title = {Structural Relational Reasoning of Point Clouds},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them. In ... | [
-0.004033274482935667,
0.01888355053961277,
0.012073562480509281,
0.04188716039061546,
0.028865259140729904,
0.05094848573207855,
-0.008044813759624958,
-0.009525222703814507,
-0.03815247491002083,
-0.029026448726654053,
-0.02008356712758541,
-0.011068067513406277,
-0.0730491429567337,
0.0... |
96 | MVF-Net: Multi-View 3D Face Morphable Model Regression | [
"Fanzi Wu",
"Linchao Bao",
"Yajing Chen",
"Yonggen Ling",
"Yibing Song",
"Songnan Li",
"King Ngi Ngan",
"Wei Liu"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Wu_MVF-Net_Multi-View_3D_Face_Morphable_Model_Regression_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_MVF-Net_Multi-View_3D_Face_Morphable_Model_Regression_CVPR_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPR_2019/supplemental/Wu_MVF-Net_Multi-View_3D_CVPR_2019_supplemental.zip | 1904.04473 | title_snapshot | @InProceedings{Wu_2019_CVPR,author = {Wu, Fanzi and Bao, Linchao and Chen, Yajing and Ling, Yonggen and Song, Yibing and Li, Songnan and Ngan, King Ngi and Liu, Wei},title = {MVF-Net: Multi-View 3D Face Morphable Model Regression},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the sing... | [
0.00975172407925129,
-0.012465138919651508,
-0.008894014172255993,
0.010330413468182087,
0.02425086498260498,
0.052872031927108765,
0.036642733961343765,
-0.021892962977290154,
-0.03209906071424484,
-0.0762292817234993,
-0.00242752768099308,
0.010957655496895313,
-0.07565280795097351,
0.02... |
97 | Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction | [
"Chen-Hsuan Lin",
"Oliver Wang",
"Bryan C. Russell",
"Eli Shechtman",
"Vladimir G. Kim",
"Matthew Fisher",
"Simon Lucey"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Lin_Photometric_Mesh_Optimization_for_Video-Aligned_3D_Object_Reconstruction_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Lin_Photometric_Mesh_Optimization_for_Video-Aligned_3D_Object_Reconstruction_CVPR_2019_paper.pdf | null | 1903.08642 | title_snapshot | @InProceedings{Lin_2019_CVPR,author = {Lin, Chen-Hsuan and Wang, Oliver and Russell, Bryan C. and Shechtman, Eli and Kim, Vladimir G. and Fisher, Matthew and Lucey, Simon},title = {Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction},booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | In this paper, we address the problem of 3D object mesh reconstruction from RGB videos. Our approach combines the best of multi-view geometric and data-driven methods for 3D reconstruction by optimizing object meshes for multi-view photometric consistency while constraining mesh deformations with a shape prior. We pose... | [
0.028868552297353745,
0.035402603447437286,
-0.006089166272431612,
0.04050702974200249,
0.013288886286318302,
0.07389043271541595,
0.022700084373354912,
-0.007582124322652817,
-0.04084273427724838,
-0.07435180991888046,
-0.02980918064713478,
-0.008932040072977543,
-0.0636674165725708,
-0.0... |
98 | Guided Stereo Matching | [
"Matteo Poggi",
"Davide Pallotti",
"Fabio Tosi",
"Stefano Mattoccia"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Poggi_Guided_Stereo_Matching_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Poggi_Guided_Stereo_Matching_CVPR_2019_paper.pdf | null | 1905.10107 | title_snapshot | @InProceedings{Poggi_2019_CVPR,author = {Poggi, Matteo and Pallotti, Davide and Tosi, Fabio and Mattoccia, Stefano},title = {Guided Stereo Matching},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}} | Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments.... | [
0.016887538135051727,
-0.00042290997225791216,
-0.017019836232066154,
0.048901382833719254,
0.022444434463977814,
0.07099202275276184,
0.01552131399512291,
0.00826012622565031,
-0.006185886450111866,
-0.05317244678735733,
-0.03316399082541466,
0.013773075304925442,
-0.0598495751619339,
0.0... |
99 | Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion | [
"Alex Zihao Zhu",
"Liangzhe Yuan",
"Kenneth Chaney",
"Kostas Daniilidis"
] | https://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Unsupervised_Event-Based_Learning_of_Optical_Flow_Depth_and_Egomotion_CVPR_2019_paper.html | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Unsupervised_Event-Based_Learning_of_Optical_Flow_Depth_and_Egomotion_CVPR_2019_paper.pdf | null | 1812.08156 | title_snapshot | @InProceedings{Zhu_2019_CVPR,author = {Zhu, Alex Zihao and Yuan, Liangzhe and Chaney, Kenneth and Daniilidis, Kostas},title = {Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion},booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},ye... | In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that maintains the temporal distribution of the events, which we pass thro... | [
0.030843796208500862,
-0.013365326449275017,
0.0018865125020965934,
0.0376133993268013,
0.03289683908224106,
0.022605109959840775,
0.010904019698500633,
0.008306375704705715,
-0.03822668269276619,
-0.028040757402777672,
-0.008223442360758781,
0.015976952388882637,
-0.06119745224714279,
-0.... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.