1 code implementation • 7 May 2024 • Ziqi Zhou, Jingyue Zhang, Jingyuan Zhang, Boyue Wang, Tianyu Shi, Alaa Khamis
One of the key challenges in current Reinforcement Learning (RL)-based Automated Driving (AD) agents is achieving flexible, precise, and human-like behavior cost-effectively.
no code implementations • 28 Mar 2024 • Jiapu Wang, Zheng Cui, Boyue Wang, Shirui Pan, Junbin Gao, BaoCai Yin, Wen Gao
However, existing Temporal Knowledge Graph Completion (TKGC) methods either model TKGs in a single space or neglect the heterogeneity of different curvature spaces, thus constraining their capacity to capture these intricate geometric structures.
no code implementations • 4 Mar 2024 • Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang, Haiyang Yu, Zhiyong Cui
Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management.
1 code implementation • CVPR 2023 • Xiaoyan Li, Gang Zhang, Boyue Wang, Yongli Hu, BaoCai Yin
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time.
1 code implementation • 4 Aug 2023 • Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, BaoCai Yin, Wen Gao
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
1 code implementation • 18 Jan 2021 • Guangyu Huo, Yong Zhang, Junbin Gao, Boyue Wang, Yongli Hu, BaoCai Yin
In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks.
no code implementations • 17 May 2017 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Subspace data representation has recently become a common practice in many computer vision tasks.
no code implementations • 27 Apr 2017 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Haoran Chen, Bao-Cai Yin
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos.
no code implementations • 13 Jun 2016 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering.
no code implementations • 21 Jan 2016 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
As a significant subspace clustering method, low rank representation (LRR) has attracted great attention in recent years.
no code implementations • 9 Jan 2016 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
The novelty of this paper is to generalize LRR on Euclidean space onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework.
no code implementations • 8 Apr 2015 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to.
no code implementations • 8 Apr 2015 • Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Many computer vision algorithms employ subspace models to represent data.