H
Houqiang Li
Researcher at University of Science and Technology of China
Publications - 612
Citations - 17591
Houqiang Li is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Motion compensation. The author has an hindex of 57, co-authored 520 publications receiving 12325 citations. Previous affiliations of Houqiang Li include China University of Science and Technology & Nanjing Medical University.
Papers
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Proceedings ArticleDOI
Click-through-based cross-view learning for image search
TL;DR: This paper proposes a novel cross-view learning method for image search, named Click-through-based Cross-view Learning (CCL), by jointly minimizing the distance between the mappings of query and image in the latent subspace and preserving the inherent structure in each original space.
Journal ArticleDOI
An Efficient Four-Parameter Affine Motion Model for Video Coding
TL;DR: A simplified affine motion model-based coding framework to overcome the limitation of a translational motion model and maintain low-computational complexity is studied.
Proceedings ArticleDOI
Sign language recognition with long short-term memory
Tao Liu,Wengang Zhou,Houqiang Li +2 more
TL;DR: An end-to-end method for SLR based on Long Short-Term memory (LSTM), which takes the moving trajectories of 4 skeleton joints as inputs without any prior knowledge and is free of explicit feature design.
Journal ArticleDOI
SIFT match verification by geometric coding for large-scale partial-duplicate web image search
TL;DR: This article proposes a novel geometric coding algorithm, to encode the spatial context among local features for large-scale partial-duplicate Web image retrieval, which achieves comparable performance to other state-of-the-art global geometric verification methods, but is more computationally efficient.
Proceedings ArticleDOI
Dilated convolutional network with iterative optimization for continuous sign language recognition
TL;DR: A novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition and experimental results on RWTH-PHOENIX-Weather demonstrate the advantages and effectiveness of the proposed method.