3k citations · 3.2k across the 22 of their papers we have counts for
22 papers
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Liang Zhao, En Yu, Zheng Ge +8
Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact…
GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Zhuoling Li, Chunrui Han, Zheng Ge +5
Efficiency is quite important for 3D lane detection due to practical deployment demand. In this work, we propose a simple, fast, and end-to-end detector that still maintains high d…
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection
Weixin Mao, Jinrong Yang, Zheng Ge +5
Depth perception is a crucial component of monoc-ular 3D detection tasks that typically involve ill-posed problems. In light of the success of sample mining techniques in 2D object…
The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge
Dongming Wu, Fan Jia, Jiahao Chang +7
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge. Considering that topology reasoning is based on centerline detection and traffic element det…
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo
Yinhao Li, Jinrong Yang, Jianjian Sun +3
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal mu…
Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception
Chunrui Han, Jinrong Yang, Jianjian Sun +6
Long-term temporal fusion is a crucial but often overlooked technique in camera-based Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner. While p…
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Zekun Qi, Runpei Dong, Guofan Fan +4
Mainstream 3D representation learning approaches are built upon contrastive or generative modeling pretext tasks, where great improvements in performance on various downstream task…
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
Runpei Dong, Zekun Qi, Linfeng Zhang +5
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natura…
MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
Hongyu Zhou, Zheng Ge, Zeming Li +1
This paper proposes an efficient multi-camera to Bird's-Eye-View (BEV) view transformation method for 3D perception, dubbed MatrixVT. Existing view transformers either suffer from…
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…
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
Yinhao Li, Han Bao, Zheng Ge +3
Bounded by the inherent ambiguity of depth perception, contemporary camera-based 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal…
STS: Surround-view Temporal Stereo for Multi-view 3D Detection
Zengran Wang, Chen Min, Zheng Ge +4
Learning accurate depth is essential to multi-view 3D object detection. Recent approaches mainly learn depth from monocular images, which confront inherent difficulties due to the…
PersDet: Monocular 3D Detection in Perspective Bird's-Eye-View
Hongyu Zhou, Zheng Ge, Weixin Mao +1
Currently, detecting 3D objects in Bird's-Eye-View (BEV) is superior to other 3D detectors for autonomous driving and robotics. However, transforming image features into BEV necess…
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
Hongyu Zhou, Zheng Ge, Songtao Liu +4
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this wo…
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
Yinhao Li, Zheng Ge, Guanyi Yu +5
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection. Our work is…
YOLOX: Exceeding YOLO Series in 2021
Zheng Ge, Songtao Liu, Feng Wang +2
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and…
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
Songyang Zhang, Lin Song, Songtao Liu +4
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YO…
OTA: Optimal Transport Assignment for Object Detection
Zheng Ge, Songtao Liu, Zeming Li +2
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we…
LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
Zheng Ge, Jianfeng Wang, Xin Huang +2
Label assignment has been widely studied in general object detection because of its great impact on detectors' performance. However, none of these works focus on label assignment i…
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
Zheng Ge, Zequn Jie, Xin Huang +2
Imbalance issue is a major yet unsolved bottleneck for the current object detection models. In this work, we observe two crucial yet never discussed imbalance issues. The first imb…
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
Xin Huang, Zheng Ge, Zequn Jie +1
Although significant progress has been made in pedestrian detection recently, pedestrian detection in crowded scenes is still challenging. The heavy occlusion between pedestrians i…
PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression
Zheng Ge, Zequn Jie, Xin Huang +2
Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly p…