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Yuanjun Xiong
Researcher at The Chinese University of Hong Kong
Publications - 8
Citations - 1259
Yuanjun Xiong is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 7, co-authored 8 publications receiving 1069 citations.
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Towards Good Practices for Very Deep Two-Stream ConvNets
TL;DR: This report presents very deep two-stream ConvNets for action recognition, by adapting recent very deep architectures into video domain, and extends the Caffe toolbox into Multi-GPU implementation with high computational efficiency and low memory consumption.
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DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
Wanli Ouyang,Ping Luo,Zeng Xingyu,Shi Qiu,Yonglong Tian,Hongsheng Li,Shuo Yang,Zhe Wang,Yuanjun Xiong,Chen Qian,Zhenyao Zhu,Ruohui Wang,Chen Change Loy,Xiaogang Wang,Xiaoou Tang +14 more
TL;DR: A set of models with large diversity are obtained, which significantly improves the effectiveness of modeling averaging, and improves the mean averaged precision obtained by RCNN, which is the state of the art of object detection, from31% to 45%.
Posted Content
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016.
Yuanjun Xiong,Limin Wang,Zhe Wang,Bowen Zhang,Hang Song,Wei Li,Dahua Lin,Yu Qiao,Luc Van Gool,Xiaoou Tang +9 more
TL;DR: This paper uses the latest deep model architecture, e.g., ResNet and Inception V3, and introduces new aggregation schemes (top-k and attention-weighted pooling) and incorporates the audio as a complementary channel, extracting relevant information via a CNN applied to the spectrograms.
Proceedings ArticleDOI
Recognize complex events from static images by fusing deep channels
TL;DR: Inspired by the recent success of deep learning, a multi-layer framework is formulated to tackle the problem of event recognition, which takes into account both visual appearance and the interactions among humans and objects and combines them via semantic fusion.
Journal ArticleDOI
Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs
TL;DR: Wang et al. as mentioned in this paper proposed a multi-resolution CNN architecture that captures visual content and structure at multiple levels and designed two knowledge guided disambiguation techniques to deal with the problem of label ambiguity.