CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id uint32 0 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 |
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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 ... | [
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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... | [
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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... | [
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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... | [
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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... | [
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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... | [
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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... | [
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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... | [
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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... | [
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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 ... | [
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