Xinggang Wang   Associate professor

王兴刚,华中科技大学,电信息学院,华中卓越学者晨星岗副教授。主要研究方向为计算机视觉和深度学习,研究工作见:https://xinggangw.info。分别于2009年和2014年在华中科技大学获得学士和博士学位,博士期间在美国天普大学、加州大学洛杉矶分校(UCLA)访问研究。在IEEE TPAMI、CVPR、ICML等顶级期刊会议发表学术论文50余篇。谷歌学术引用次数超过7000次。担任CVPR 2022领域主席,Pattern Recognition (IF 7.196)、Image and ...Detials

Fu Q*, Xu B, Parikh D, Cervantes D, Xiang YK. Insulin induces IRS2-dependent and GRK2-mediated β2AR internalization to attenuate βAR signaling in cardiomyocytes. Cell Signal. 2015 Mar;27(3):707-15 (*corresponding author)

Release time:2021-06-10  Hits:

  • Indexed by:会议论文
  • First Author:Wang,Xinggang,Wang,Xinggang
  • Correspondence Author:Wang,Xinggang
  • Co-author:Latecki,Jan,Longin,Liu,Wenyu,Yang,Xingwei,Bai,Xiang
  • Journal:Advances in Neural Information Processing Systems 24 (NIPS 2011)
  • Date of Publication:2011-12-12
  • Abstract:We propose a novel inference framework for finding maximal cliques in a weighted graph that satisfy hard constraints. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., sets of nodes that cannot belong to the same solution. The proposed inference is based on a novel particle filter algorithm with state permeations. We apply the inference framework to a challenging problem of learning part-based, deformable object models. Two core problems in the learning framework, matching of image patches and finding salient parts, are formulated as two instances of the problem of finding maximal cliques with hard constraints. Our learning framework yields discriminative part based object models that achieve very good detection rate, and outperform other methods on object classes with large deformation.