Search Results for author: Liang Du

Found 15 papers, 3 papers with code

SGM3D: Stereo Guided Monocular 3D Object Detection

1 code implementation3 Dec 2021 Zheyuan Zhou, Liang Du, Xiaoqing Ye, Zhikang Zou, Xiao Tan, Errui Ding, Li Zhang, xiangyang xue, Jianfeng Feng

We innovatively investigate a multi-granularity domain adaptation module (MG-DA) to exploit the network's ability so as to generate stereo-mimic features only based on the monocular cues.

Autonomous Driving Domain Adaptation +1

A Data-Driven Democratized Control Architecture for Regional Transmission Operators

no code implementations20 Sep 2021 Xiaoyuan Fan, Daniel Moscovitz, Liang Du, Walid Saad

As probably the most complicated and critical infrastructure system, U. S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users.

MM-Deacon: Multimodal molecular domain embedding analysis via contrastive learning

no code implementations18 Sep 2021 Zhihui Guo, Pramod Kumar Sharma, Liang Du, Robin Abraham

First, SMILES and IUPAC strings are encoded by using two different transformer-based language models independently, then the contrastive loss is utilized to bring these encoded representations from different modalities closer to each other if they belong to the same molecule, and to push embeddings farther from each other if they belong to different molecules.

Contrastive Learning Language Modelling +1

Manifold Adaptive Multiple Kernel K-Means for Clustering

no code implementations30 Sep 2020 Liang Du, Haiying Zhang, Xin Ren, Xiaolin Lv

Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering.

3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection

no code implementations1 Mar 2020 Liang Du, Jingang Tan, xiangyang xue, Lili Chen, Hongkai Wen, Jianfeng Feng, Jiamao Li, Xiaolin Zhang

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.

3D Semantic Instance Segmentation Feature Selection +1

Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE

no code implementations4 Nov 2019 Shen Zhang, Shibo Zhang, Sufei Li, Liang Du, Thomas G. Habetler

However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification.

SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation

no code implementations ICCV 2019 Liang Du, Jingang Tan, Hongye Yang, Jianfeng Feng, Xiangyang Xue, Qibao Zheng, Xiaoqing Ye, Xiaolin Zhang

Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations.

Domain Adaptation Semantic Segmentation

Cross-Age Face Verification by Coordinating With Cross-Face Age Verification

no code implementations CVPR 2015 Liang Du, Haibin Ling

As shown in our experiments, the algorithm effectively balances feature sharing and feature exclusion between the two tasks; and, for face verification, the algorithm effectively removes distracting features used in age verification.

Face Verification Feature Selection

Unsupervised Feature Selection with Adaptive Structure Learning

1 code implementation3 Apr 2015 Liang Du, Yi-Dong Shen

Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data.

Feature Selection

Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval

no code implementations25 Sep 2014 Liang Wu, Hui Xiong, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li

Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.

Information Retrieval Metric Learning

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