Search Results for author: Yingfei Liu

Found 13 papers, 8 papers with code

Stream Query Denoising for Vectorized HD Map Construction

no code implementations17 Jan 2024 Shuo Wang, Fan Jia, Yingfei Liu, Yucheng Zhao, Zehui Chen, Tiancai Wang, Chi Zhang, Xiangyu Zhang, Feng Zhao

This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction.

Autonomous Driving Denoising

Panacea: Panoramic and Controllable Video Generation for Autonomous Driving

no code implementations28 Nov 2023 Yuqing Wen, Yucheng Zhao, Yingfei Liu, Fan Jia, Yanhui Wang, Chong Luo, Chi Zhang, Tiancai Wang, Xiaoyan Sun, Xiangyu Zhang

This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.

Autonomous Driving Video Generation

ADriver-I: A General World Model for Autonomous Driving

no code implementations22 Nov 2023 Fan Jia, Weixin Mao, Yingfei Liu, Yucheng Zhao, Yuqing Wen, Chi Zhang, Xiangyu Zhang, Tiancai Wang

Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I.

Autonomous Driving

VLM-Eval: A General Evaluation on Video Large Language Models

no code implementations20 Nov 2023 Shuailin Li, Yuang Zhang, Yucheng Zhao, Qiuyue Wang, Fan Jia, Yingfei Liu, Tiancai Wang

Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent.

Action Recognition Retrieval

Language Prompt for Autonomous Driving

2 code implementations8 Sep 2023 Dongming Wu, Wencheng Han, Tiancai Wang, Yingfei Liu, Xiangyu Zhang, Jianbing Shen

A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt.

Autonomous Driving Object

Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection

1 code implementation ICCV 2023 Shihao Wang, Yingfei Liu, Tiancai Wang, Ying Li, Xiangyu Zhang

On the standard nuScenes benchmark, it is the first online multi-view method that achieves comparable performance (67. 6% NDS & 65. 3% AMOTA) with lidar-based methods.

3D Multi-Object Tracking 3D Object Detection +2

Cannot find the paper you are looking for? You can Submit a new open access paper.