1 code implementation • 15 Sep 2023 • Yupeng Jia, Jie He, Runze Chen, Fang Zhao, Haiyong Luo
Visual-based 3D semantic occupancy perception (also known as 3D semantic scene completion) is a new perception paradigm for robotic applications like autonomous driving.
1 code implementation • 27 Jul 2023 • Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, Ding Zhao, Liangjun Zhang, Hesheng Wang, Wei Tsang Ooi, Ruijie Zhu, Ziyang Song, Li Liu, Tianzhu Zhang, Jun Yu, Mohan Jing, Pengwei Li, Xiaohua Qi, Cheng Jin, Yingfeng Chen, Jie Hou, Jie Zhang, Zhen Kan, Qiang Ling, Liang Peng, Minglei Li, Di Xu, Changpeng Yang, Yuanqi Yao, Gang Wu, Jian Kuai, Xianming Liu, Junjun Jiang, Jiamian Huang, Baojun Li, Jiale Chen, Shuang Zhang, Sun Ao, Zhenyu Li, Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
1 code implementation • IEEE 39th International Conference on Data Engineering (ICDE) 2023 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang
To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied.
Ranked #1 on Traffic Prediction on PeMS08
1 code implementation • 20 Jan 2022 • Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, Yuchen Fang
To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module.
no code implementations • 6 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.
no code implementations • 6 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang
Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.
no code implementations • 4 Dec 2021 • Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang
In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously.
no code implementations • 4 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Chenxing Wang
Traffic prediction has gradually attracted the attention of researchers because of the increase in traffic big data.
no code implementations • 6 Jul 2021 • Runze Chen, Haiyong Luo, Fang Zhao, Xuechun Meng, Zhiqing Xie, Yida Zhu
The comparative experiments of knowledge distillation on six public datasets also demonstrate that the SMLDist outperforms other advanced knowledge distillation methods of students' performance, which verifies the good generalization of the SMLDist on other classification tasks, including but not limited to HAR.
no code implementations • 20 Jan 2021 • Zhuqing Jiang, Chang Liu, Ya'nan Wang, Kai Li, Aidong Men, Haiying Wang, Haiyong Luo
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.