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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Journal ArticleDOI
Intermediate description for multiple video adaptation
TL;DR: Experimental results demonstrate that intermedia can support multiple video adaptations rapidly while maintaining coding efficiency, and is considered as a novel framework for video adaptation.
Proceedings ArticleDOI
Visual query compression with locality preserving projection on Grassmann manifold
TL;DR: A new approach in visual key points compression is examined, that utilizes subspaces that optimized for preserving key point feature matching properties than the reconstruction performance, and allows for a set of optimal subspaced on Grassmann manifold that can better adapt to the local manifold geometry.
Posted Content
Efficient Integer-Arithmetic-Only Convolutional Neural Networks.
TL;DR: This work designs a mechanism to handle the cases of feature map addition and feature map concatenation in integer-arithmetic-only networks and proposes an empirical rule to tune the bound of each Bounded ReLU.
Journal ArticleDOI
Comments on “Approximate Characterizations for the Gaussian Source Broadcast Distortion Region”
Lei Yu,Houqiang Li,Weiping Li +2 more
TL;DR: This correspondence proves that for the bandwidth expansion case (with K ≥ 2), this outer bound is strictly tighter than the trivial outer bound with each user being optimal in the point-to-point setting; while for thewidth compression or bandwidth match case, this outer Bound actually degenerates to the trivialouter bound.
Journal ArticleDOI
Single-stage Instance Segmentation
TL;DR: This work proposes a single-stage framework to tackle the instance segmentation task, and is the first attempt to segment instances in asingle-stage pipeline on challenging datasets.