162 citations · 354 across the 26 of their papers we have counts for
28 papers
Online Map Vectorization for Autonomous Driving: A Rasterization Perspective
Gongjie Zhang, Jiahao Lin, Shuang Wu +5
Vectorized high-definition (HD) map is essential for autonomous driving, providing detailed and precise environmental information for advanced perception and planning. However, cur…
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…
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
Changqing Zhou, Zhipeng Luo, Yueru Luo +5
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by…
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
Kaiwen Cui, Jiaxing Huang, Zhipeng Luo +3
Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-…
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
Manqing Dong, Chunguang Pan, Zhipeng Luo
Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works tr…
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
Zhipeng Luo, Zhongang Cai, Changqing Zhou +7
Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. However, drastic performance degradation rema…
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Dawei Du, Longyin Wen, Pengfei Zhu +52
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.…
Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Zhipeng Luo, Xiaobing Zhang, Shijian Lu +1
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA),…
NTIRE 2021 Depth Guided Image Relighting Challenge
Majed El Helou, Ruofan Zhou, Sabine Susstrunk +1
Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalizati…
BERT-based Acronym Disambiguation with Multiple Training Strategies
Chunguang Pan, Bingyan Song, Shengguang Wang +1
Acronym disambiguation (AD) task aims to find the correct expansions of an ambiguous ancronym in a given sentence. Although it is convenient to use acronyms, sometimes they could b…
DA-DETR: Domain Adaptive Detection Transformer with Information Fusion
Jingyi Zhang, Jiaxing Huang, Zhipeng Luo +3
The recent detection transformer (DETR) simplifies the object detection pipeline by removing hand-crafted designs and hyperparameters as employed in conventional two-stage object d…
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
Gongjie Zhang, Zhipeng Luo, Kaiwen Cui +1
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is constra…
Semantic Segmentation with Labeling Uncertainty and Class Imbalance
Patrik Olã Bressan, José Marcato Junior, José Augusto Correa Martins +8
Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the u…
Counting and Locating High-Density Objects Using Convolutional Neural Network
Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta +8
This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
A Technical Report for VIPriors Image Classification Challenge
Zhipeng Luo, Ge Li, Zhiguang Zhang
Image classification has always been a hot and challenging task. This paper is a brief report to our submission to the VIPriors Image Classification Challenge. In this challenge, t…
Challenge report:VIPriors Action Recognition Challenge
Zhipeng Luo, Dawei Xu, Zhiguang Zhang
This paper is a brief report to our submission to the VIPriors Action Recognition Challenge. Action recognition has attracted many researchers attention for its full application, b…
VIPriors Object Detection Challenge
Zhipeng Luo, Lixuan Che
This paper is a brief report to our submission to the VIPriors Object Detection Challenge. Object Detection has attracted many researchers' attention for its full application, but…