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Kaiming He
Researcher at Facebook
Publications - 140
Citations - 440003
Kaiming He is an academic researcher from Facebook. The author has contributed to research in topics: Object detection & Image segmentation. The author has an hindex of 89, co-authored 135 publications receiving 272091 citations. Previous affiliations of Kaiming He include The Chinese University of Hong Kong & Microsoft.
Papers
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Proceedings Article
R-FCN: Object Detection via Region-based Fully Convolutional Networks
TL;DR: R-FCN as mentioned in this paper proposes position-sensitive score maps to address the dilemma between translation-invariance in image classification and translation-variance in object detection, and achieves state-of-the-art performance on the PASCAL VOC dataset.
Posted Content
Momentum Contrast for Unsupervised Visual Representation Learning
TL;DR: This article proposed Momentum Contrast (MoCo) for unsupervised visual representation learning, which enables building a large and consistent dictionary on-the-fly that facilitates contrastive learning.
Proceedings ArticleDOI
Momentum Contrast for Unsupervised Visual Representation Learning
TL;DR: This article proposed Momentum Contrast (MoCo) for unsupervised visual representation learning, which enables building a large and consistent dictionary on-the-fly that facilitates contrastive learning.
Book ChapterDOI
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
TL;DR: This work equips the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement, and develops a new network structure, called SPP-net, which can generate a fixed-length representation regardless of image size/scale.
Journal ArticleDOI
Single Image Haze Removal Using Dark Channel Prior
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel.