Q
Qionghai Dai
Researcher at Tsinghua University
Publications - 777
Citations - 22034
Qionghai Dai is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Iterative reconstruction. The author has an hindex of 68, co-authored 715 publications receiving 16435 citations. Previous affiliations of Qionghai Dai include McGovern Institute for Brain Research & MediaTech Institute.
Papers
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Journal ArticleDOI
3-D Object Retrieval and Recognition With Hypergraph Analysis
TL;DR: A hypergraph analysis approach to address the problem of view-based 3-D object retrieval and recognition by avoiding the estimation of the distance between objects by constructing multiple hypergraphs based on their 2-D views.
Journal ArticleDOI
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
TL;DR: This work introduces a recurrent deep neural network for real-time financial signal representation and trading and proposes a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training.
Proceedings ArticleDOI
Covariance discriminative learning: A natural and efficient approach to image set classification
TL;DR: A novel discriminative learning approach to image set classification by modeling the image set with its natural second-order statistic, i.e. covariance matrix, which shows the superiority of this method over state-of-the-art ones in both accuracy and efficiency, but also its stability to two real challenges: noisy set data and varying set size.
Journal ArticleDOI
Light Field Image Processing: An Overview
Gaochang Wu,Belen Masia,Adrian Jarabo,Yuchen Zhang,Liangyong Wang,Qionghai Dai,Tianyou Chai,Yebin Liu +7 more
TL;DR: A comprehensive overview and discussion of research in light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data are presented.
Journal ArticleDOI
Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors
TL;DR: This paper analyzes the ME structure in HEVC and proposes a parallel framework to decouple ME for different partitions on many-core processors and achieves more than 30 and 40 times speedup for 1920 × 1080 and 2560 × 1600 video sequences, respectively.