Search Results for author: Xiangru Huang

Found 8 papers, 4 papers with code

Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs

no code implementations NeurIPS 2020 Xiangru Huang, Haitao Yang, Etienne Vouga, QiXing Huang

We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model.

Joint Learning of Neural Networks via Iterative Reweighted Least Squares

1 code implementation16 May 2019 Zaiwei Zhang, Xiangru Huang, Qi-Xing Huang, Xiao Zhang, Yuan Li

We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.

General Classification Image Classification +1

Learning Transformation Synchronization

1 code implementation CVPR 2019 Xiangru Huang, Zhenxiao Liang, Xiaowei Zhou, Yao Xie, Leonidas Guibas, Qi-Xing Huang

Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.

Joint Map and Symmetry Synchronization

no code implementations ECCV 2018 Yifan Sun, Zhenxiao Liang, Xiangru Huang, Qi-Xing Huang

Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.

Translation Synchronization via Truncated Least Squares

no code implementations NeurIPS 2017 Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qi-Xing Huang

In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.


Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain

no code implementations NeurIPS 2016 Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep K. Ravikumar, Inderjit S. Dhillon

In this work, we show that, by decomposing training of Structural Support Vector Machine (SVM) into a series of multiclass SVM problems connected through messages, one can replace expensive structured oracle with Factorwise Maximization Oracle (FMO) that allows efficient implementation of complexity sublinear to the factor domain.

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification

1 code implementation ICML 2016 Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon

In this work, we show that a margin-maximizing loss with l1 penalty, in case of Extreme Classification, yields extremely sparse solution both in primal and in dual without sacrificing the expressive power of predictor.

General Classification Text Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.