J
Jiebo Luo
Researcher at University of Rochester
Publications - 967
Citations - 41334
Jiebo Luo is an academic researcher from University of Rochester. The author has contributed to research in topics: Computer science & Social media. The author has an hindex of 83, co-authored 893 publications receiving 31341 citations. Previous affiliations of Jiebo Luo include Eastman Kodak Company & Xerox.
Papers
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Journal ArticleDOI
Learning multi-label scene classification
TL;DR: A framework to handle semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels, is presented and appears to generalize to other classification problems of the same nature.
Proceedings ArticleDOI
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
Gui-Song Xia,Xiang Bai,Jian Ding,Zhen Zhu,Serge Belongie,Jiebo Luo,Mihai Datcu,Marcello Pelillo,Liangpei Zhang +8 more
TL;DR: The Dataset for Object Detection in Aerial Images (DOTA) as discussed by the authors is a large-scale dataset of aerial images collected from different sensors and platforms and contains objects exhibiting a wide variety of scales, orientations, and shapes.
Proceedings ArticleDOI
Image Captioning with Semantic Attention
TL;DR: Zhang et al. as discussed by the authors proposed a model of semantic attention to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. But their model is not suitable for image caption generation.
Posted Content
Image Captioning with Semantic Attention
TL;DR: This paper proposes a new algorithm that combines top-down and bottom-up approaches to natural language description through a model of semantic attention, and significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.
Proceedings ArticleDOI
Recognizing realistic actions from videos “in the wild”
TL;DR: This paper presents a systematic framework for recognizing realistic actions from videos “in the wild”, and uses motion statistics to acquire stable motion features and clean static features, and PageRank is used to mine the most informative static features.