22 citations · 79 across the 25 of their papers we have counts for
21 papers · 1 filter
PADriver: Towards Personalized Autonomous Driving
Genghua Kou, Fan Jia, Weixin Mao +7
In this paper, we propose PADriver, a novel closed-loop framework for personalized autonomous driving (PAD). Built upon Multi-modal Large Language Model (MLLM), PADriver takes stre…
Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models
Meng Cao, Yuyang Liu, Yingfei Liu +6
Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language Models (LVLMs) to meet individual task requirements. To date, most of the existing approach…
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework…
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?
Yifan Bai, Dongming Wu, Yingfei Liu +8
Rapid advancements in Autonomous Driving (AD) tasks turned a significant shift toward end-to-end fashion, particularly in the utilization of vision-language models (VLMs) that inte…
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Binyuan Huang, Yuqing Wen, Yucheng Zhao +9
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data…
Stream Query Denoising for Vectorized HD Map Construction
Shuo Wang, Fan Jia, Yingfei Liu +6
To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particul…
Bootstrap Masked Visual Modeling via Hard Patches Mining
Haochen Wang, Junsong Fan, Yuxi Wang +4
Masked visual modeling has attracted much attention due to its promising potential in learning generalizable representations. Typical approaches urge models to predict specific con…
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and contr…
ADriver-I: A General World Model for Autonomous Driving
Fan Jia, Weixin Mao, Yingfei Liu +5
Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular desi…
Language Prompt for Autonomous Driving
Dongming Wu, Wencheng Han, Yingfei Liu +4
A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of u…
Far3D: Expanding the Horizon for Surround-view 3D Object Detection
Xiaohui Jiang, Shuailin Li, Yingfei Liu +5
Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost. However, most works have primarily focused on close perception ra…
OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation
Dongming Wu, Tiancai Wang, Yuang Zhang +2
Referring video object segmentation (RVOS) aims at segmenting an object in a video following human instruction. Current state-of-the-art methods fall into an offline pattern, in wh…
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
En Yu, Tiancai Wang, Zhuoling Li +3
Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisf…
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Yukang Chen, Jianhui Liu, Xiangyu Zhang +2
3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified…
Referring Multi-Object Tracking
Dongming Wu, Wencheng Han, Tiancai Wang +3
Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, ter…
Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
Shihao Wang, Yingfei Liu, Tiancai Wang +2
In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection. Built upon the sparse query design in the PETR series, we system…
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
Junjie Yan, Yingfei Liu, Jianjian Sun +4
In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the im…
Towards 3D Object Detection with 2D Supervision
Jinrong Yang, Tiancai Wang, Zheng Ge +3
The great progress of 3D object detectors relies on large-scale data and 3D annotations. The annotation cost for 3D bounding boxes is extremely expensive while the 2D ones are easi…
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
Yuang Zhang, Tiancai Wang, Xiangyu Zhang
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, MOTR…
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
Yuang Zhang, Tiancai Wang, Weiyao Lin +1
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge. Based on MOTR: End-to-End Multiple-Object Tracking with Transformer, we explore: 1) detect…
Implicit Feature Refinement for Instance Segmentation
Lufan Ma, Tiancai Wang, Bin Dong +3
We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolut…