162 citations · 354 across the 26 of their papers we have counts for
7 papers · 1 filter
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention
Zhipeng Luo, Changqing Zhou, Gongjie Zhang +1
3D object detection with surround-view images is an essential task for autonomous driving. In this work, we propose DETR4D, a Transformer-based framework that explores sparse atten…
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
Zhipeng Luo, Gongjie Zhang, Changqing Zhou +3
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics. However, most existing studies focus on s…
Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors
Gongjie Zhang, Zhipeng Luo, Zichen Tian +3
Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transfo…
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
Gongjie Zhang, Zhipeng Luo, Kaiwen Cui +2
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still c…
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion
Gongjie Zhang, Zhipeng Luo, Jiaxing Huang +2
The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection. However, DETR suffers from slow training convergence, which hin…
Accelerating DETR Convergence via Semantic-Aligned Matching
Gongjie Zhang, Zhipeng Luo, Yingchen Yu +2
The recently developed DEtection TRansformer (DETR) establishes a new object detection paradigm by eliminating a series of hand-crafted components. However, DETR suffers from extre…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…