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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Proceedings ArticleDOI
Sparse projections for high-dimensional binary codes
TL;DR: Experiments show that the sparsity encouraging regularizer introduced in this paper leads to better accuracy than dense projections, and is more accurate and faster than other recently proposed methods for speeding up high-dimensional binary encoding.
Posted Content
R-FCN: Object Detection via Region-based Fully Convolutional Networks
TL;DR: This work presents region-based, fully convolutional networks for accurate and efficient object detection, and proposes position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.
Book ChapterDOI
Graph Cuts for Supervised Binary Coding
Tiezheng Ge,Kaiming He,Jian Sun +2 more
TL;DR: This paper forms supervised binary coding as a single optimization problem that involves both the encoding functions and the binary label assignment, and applies the graph cuts algorithm to address the discrete optimization problem involved, with no continuous relaxation.
Journal ArticleDOI
Rectangling panoramic images via warping
Kaiming He,Huiwen Chang,Jian Sun +2 more
TL;DR: This paper presents a content-aware warping algorithm that generates rectangular images from stitched panoramic images, and demonstrates that the results are often visually plausible, and the introduced distortion is often unnoticeable.
Proceedings ArticleDOI
Joint Inverted Indexing
TL;DR: This paper presents a method that jointly optimizes all code words in all quantizers and creates them jointly, which is faster and more accurate than a recent state-of-the-art inverted indexing method.