K
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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Exploring the Limits of Weakly Supervised Pretraining
Dhruv Mahajan,Ross Girshick,Vignesh Ramanathan,Kaiming He,Manohar Paluri,Yixuan Li,Ashwin Bharambe,Laurens van der Maaten +7 more
TL;DR: This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date.
Posted Content
Deep Hough Voting for 3D Object Detection in Point Clouds
TL;DR: This work proposes VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting that achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency.
Proceedings ArticleDOI
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes
Kaiming He,Fang Wen,Jian Sun +2 more
TL;DR: A novel Affinity-Preserving K-means algorithm which simultaneously performs k-mean clustering and learns the binary indices of the quantized cells and outperforms various state-of-the-art hashing encoding methods.
Proceedings ArticleDOI
Rethinking ImageNet Pre-Training
TL;DR: In this paper, the authors report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge.
Proceedings ArticleDOI
Optimized Product Quantization for Approximate Nearest Neighbor Search
TL;DR: This paper optimization product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks and presents two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method guarantees the optimal solution if the input data follows some Gaussian distribution.