activity
20202026
most citedScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors

8 citations · 12 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Unlocking air traffic flow prediction through microscopic aircraft-state modeling

Bin Wang, Anqi Liu, Jiangtao Zhao +8

Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model traffic flow as aggregated ti…

cs.AI2022

Automated Urban Planning aware Spatial Hierarchies and Human Instructions

Dongjie Wang, Kunpeng Liu, Yanyong Huang +3

Traditional urban planning demands urban experts to spend considerable time and effort producing an optimal urban plan under many architectural constraints. The remarkable imaginat…

cs.LG2022★ 2 cited

CIMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection

Yanyong Huang, Zongxin Shen, Yuxin Cai +4

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods ass…

cs.LG2022

Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data

Yanyong Huang, Kejun Guo, Xiuwen Yi +2

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods ass…

cs.CL2021★ 8 cited

ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors

Huaishao Luo, Lei Ji, Yanyong Huang +3

Fusion technique is a key research topic in multimodal sentiment analysis. The recent attention-based fusion demonstrates advances over simple operation-based fusion. However, thes…

cs.LG2020★ 2 cited

Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection

Yanyong Huang, Zongxin Shen, Fuxu Cai +2

Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improv…