665 citations · 1k across the 18 of their papers we have counts for
18 papers
Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
Chang Xu, Ruixiang Zhang, Wen Yang +4
Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem. To address t…
On the Robustness of Object Detection Models on Aerial Images
Haodong He, Jian Ding, Bowen Xu +1
The robustness of object detection models is a major concern when applied to real-world scenarios. The performance of most models tends to degrade when confronted with images affec…
Towards Generic and Controllable Attacks Against Object Detection
Guopeng Li, Yue Xu, Jian Ding +1
Existing adversarial attacks against Object Detectors (ODs) suffer from two inherent limitations. Firstly, ODs have complicated meta-structure designs, hence most advanced attacks…
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation
Jian Ding, Nan Xue, Gui-Song Xia +2
Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test dat…
FreePoint: Unsupervised Point Cloud Instance Segmentation
Zhikai Zhang, Jian Ding, Li Jiang +2
Instance segmentation of point clouds is a crucial task in 3D field with numerous applications that involve localizing and segmenting objects in a scene. However, achieving satisfa…
Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection
Chang Xu, Jian Ding, Jinwang Wang +4
Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in…
Few-Shot Object Detection via Variational Feature Aggregation
Jiaming Han, Yuqiang Ren, Jian Ding +2
As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples,the learned models are usually biased to base classes and sensit…
Detecting Building Changes with Off-Nadir Aerial Images
Chao Pang, Jiang Wu, Jian Ding +2
The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the seman…
Expanding Low-Density Latent Regions for Open-Set Object Detection
Jiaming Han, Yuqiang Ren, Jian Ding +3
Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categori…
Decoupling Zero-Shot Semantic Segmentation
Jian Ding, Nan Xue, Gui-Song Xia +1
Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not been seen in the training. Existing works formulate ZS3 as a pixel-level zeroshot classific…
LUAI Challenge 2021 on Learning to Understand Aerial Images
Gui-Song Xia, Jian Ding, Ming Qian +33
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in a…
ReDet: A Rotation-equivariant Detector for Aerial Object Detection
Jiaming Han, Jian Ding, Nan Xue +1
Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitr…
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors
Jian Ding, Enze Xie, Hang Xu +4
Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learni…
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
Jian Ding, Nan Xue, Gui-Song Xia +8
In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of obj…
DetCo: Unsupervised Contrastive Learning for Object Detection
Enze Xie, Jian Ding, Wenhai Wang +5
Unsupervised contrastive learning achieves great success in learning image representations with CNN. Unlike most recent methods that focused on improving accuracy of image classifi…
Align Deep Features for Oriented Object Detection
Jiaming Han, Jian Ding, Jie Li +1
The past decade has witnessed significant progress on detecting objects in aerial images that are often distributed with large scale variations and arbitrary orientations. However…
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Jian Ding, Nan Xue, Yang Long +2
Object detection in aerial images is an active yet challenging task in computer vision because of the birdview perspective, the highly complex backgrounds, and the variant appearan…
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
Gui-Song Xia, Xiang Bai, Jian Ding +6
Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such succe…