Yuxin Wu and Kaiming He
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Image Classification share, To understand the world, we humans constantly need to relate the present...
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share, Contrastive unsupervised learning has recently shown encouraging progres... share, Large-scale data is of crucial importance for learning semantic segmenta... 23
12/03/2014 ∙ by Jifeng Dai, et al.
Image Classification
share, Both convolutional and recurrent operations are building blocks that pro... classification and object detection tasks, and report the highest ImageNet-1k 05/26/2015 ∙ by Xiangyu Zhang, et al. �Z�_E������?������(�M` Our experiments demonstrate that training for large-scale hashtag prediction leads to excellent results.
∙ arXiv code/models, Exploring Randomly Wired Neural Networks for Image Recognition Saining Xie, Alexander Kirillov, Ross Girshick, and Kaiming He Weakly and Self-supervised Learning — Part 1 From 2-D to 3-D Annotations Deep Learning at FAU.
∙ Networks, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
12/10/2015 ∙ by Kaiming He, et al. Improved Baselines with Momentum Contrastive Learning, PointRend: Image Segmentation as Rendering, A Multigrid Method for Efficiently Training Video Models, Momentum Contrast for Unsupervised Visual Representation Learning, Deep Hough Voting for 3D Object Detection in Point Clouds, Exploring Randomly Wired Neural Networks for Image Recognition, TensorMask: A Foundation for Dense Object Segmentation, Long-Term Feature Banks for Detailed Video Understanding, Feature Denoising for Improving Adversarial Robustness, GLoMo: Unsupervisedly Learned Relational Graphs as Transferable International Conference on Computer Vision (ICCV), 2019 (Oral)
share, Training competitive deep video models is an order of magnitude slower t... Computer Vision and Pattern Recognition (CVPR), 2019 (Oral)
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10 paper PAMI version images slides videos project thesis, Outstanding Reviewer: CVPR 2015, ICCV 2015, CVPR 2017, Microsoft Research Asia Young Fellowship, 2006. ∙
arXiv, Identity Mappings in Deep Residual Networks Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
Even so, relatively little is known about the
Laurens van der Maaten, State-of-the-art visual perception models for a wide range of tasks rely on
We also perform extensive experiments that provide novel empirical data on the relationship between large-scale pretraining and transfer learning performance.
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Weakly SupervisedPretraining Dhruv Mahajan Ross Girshick Vignesh Ramanathan Kaiming He Manohar Paluri Yixuan Li Ashwin Bharambe Laurens van der Maaten Facebook Abstract.
∙ 12/12/2017 ∙ by Ilija Radosavovic, et al.
∙ (read more), Ranked #15 on State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining.
media images.
I am a recipient of the PAMI Young Researcher Award in 2018, the Best Paper Award in CVPR 2009, CVPR 2016, ICCV 2017, the Best Student Paper Award in ICCV 2017, and the Best Paper Honorable Mention in ECCV 2018.
behavior of pretraining with datasets that are multiple orders of magnitude
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We show improvements on several image ∙
In this paper, we present a unique study of transfer learning with �O�0�h���7�o{(����D>.wJMF� �ĥ7v� EI��י����6�9r�9�#� 0��zA����!
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