359 citations · 635 across the 21 of their papers we have counts for
7 papers · 1 filter
LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating
Deguo Xia, Weiming Zhang, Xiyan Liu +6
An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems.…
Exploring the Causality of End-to-End Autonomous Driving
Jiankun Li, Hao Li, Jiangjiang Liu +6
Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these model…
BEVWorld: A Multimodal World Simulator for Autonomous Driving via Scene-Level BEV Latents
Yumeng Zhang, Shi Gong, Kaixin Xiong +7
World models have attracted increasing attention in autonomous driving for their ability to forecast potential future scenarios. In this paper, we propose BEVWorld, a novel framewo…
DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation
Deguo Xia, Weiming Zhang, Xiyan Liu +5
Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-l…
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
Min Yang, Dongliang He, Miao Fan +5
Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via si…
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones
Cheng Cui, Ruoyu Guo, Yuning Du +10
Recently, research efforts have been concentrated on revealing how pre-trained model makes a difference in neural network performance. Self-supervision and semi-supervised learning…
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
Dongliang He, Xiang Zhao, Jizhou Huang +3
The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted st…