1 code implementation • 19 Aug 2024 • Mingrui Wu, Oucheng Huang, Jiayi Ji, Jiale Li, Xinyue Cai, Huafeng Kuang, Jianzhuang Liu, Xiaoshuai Sun, Rongrong Ji
In this work, we propose a training-free, trajectory-based controllable T2I approach, termed TraDiffusion.
1 code implementation • 31 Jul 2024 • Mingrui Wu, Xinyue Cai, Jiayi Ji, Jiale Li, Oucheng Huang, Gen Luo, Hao Fei, Guannan Jiang, Xiaoshuai Sun, Rongrong Ji
We observe that attention, as the core module of MLLMs, connects text prompt tokens and visual tokens, ultimately determining the final results.
no code implementations • 23 Apr 2024 • Ming Nie, Xinyue Cai, Hang Xu, Li Zhang
Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments.
2 code implementations • 13 Feb 2024 • Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Feng Wen, Wei zhang, Li Zhang
Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
2 code implementations • 31 Jan 2024 • Renyuan Peng, Xinyue Cai, Hang Xu, Jiachen Lu, Feng Wen, Wei zhang, Li Zhang
Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG.
1 code implementation • 6 Dec 2023 • Ming Nie, Renyuan Peng, Chunwei Wang, Xinyue Cai, Jianhua Han, Hang Xu, Li Zhang
Large vision-language models (VLMs) have garnered increasing interest in autonomous driving areas, due to their advanced capabilities in complex reasoning tasks essential for highly autonomous vehicle behavior.
no code implementations • ICCV 2023 • Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Hongyang Li, Feng Wen, Wei zhang, Li Zhang
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections.
no code implementations • 19 Jul 2022 • Shenghua Xu, Xinyue Cai, Bin Zhao, Li Zhang, Hang Xu, Yanwei Fu, xiangyang xue
This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution.
2 code implementations • CVPR 2022 • Fan Yan, Ming Nie, Xinyue Cai, Jianhua Han, Hang Xu, Zhen Yang, Chaoqiang Ye, Yanwei Fu, Michael Bi Mi, Li Zhang
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
no code implementations • 18 Mar 2022 • Jianhua Han, Xiajun Deng, Xinyue Cai, Zhen Yang, Hang Xu, Chunjing Xu, Xiaodan Liang
We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving.
1 code implementation • 3 Nov 2020 • Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
1 code implementation • ECCV 2020 • Hang Xu, Shaoju Wang, Xinyue Cai, Wei zhang, Xiaodan Liang, Zhenguo Li
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CurveLanes