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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…
NEAT: Distilling 3D Wireframes from Neural Attraction Fields
Nan Xue, Bin Tan, Yuxi Xiao +4
This paper studies the problem of structured 3D reconstruction using wireframes that consist of line segments and junctions, focusing on the computation of structured boundary geom…
Depth and DOF Cues Make A Better Defocus Blur Detector
Yuxin Jin, Ming Qian, Jincheng Xiong +2
Defocus blur detection (DBD) separates in-focus and out-of-focus regions in an image. Previous approaches mistakenly mistook homogeneous areas in focus for defocus blur regions, li…
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…
Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
Ming Qian, Jincheng Xiong, Gui-Song Xia +1
This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs. Our focus is on the challenging problem of 3D-aware ground…
Learning to Super-Resolve Blurry Images with Events
Lei Yu, Bishan Wang, Xiang Zhang +4
Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this paper, we…
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…
Learning to See Through with Events
Lei Yu, Xiang Zhang, Wei Liao +2
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi…
Detecting Line Segments in Motion-blurred Images with Events
Huai Yu, Hao Li, Wen Yang +2
Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D reconstruction. Existi…
NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction
Bin Tan, Nan Xue, Tianfu Wu +1
This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs…
Level-SfM: Structure from Motion on Neural Level Set of Implicit Surfaces
Yuxi Xiao, Nan Xue, Tianfu Wu +1
This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-SfM, which estimates the camera poses and scene geometry from a set of uncalibrated images…
Transformers in Remote Sensing: A Survey
Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer +4
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures,…
HoW-3D: Holistic 3D Wireframe Perception from a Single Image
Wenchao Ma, Bin Tan, Nan Xue +3
This paper studies the problem of holistic 3D wireframe perception (HoW-3D), a new task of perceiving both the visible 3D wireframes and the invisible ones from single-view 2D imag…
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images
Weijia Li, Yawen Lai, Linning Xu +5
This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, the OmniCity contains multi-view satellite ima…
RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection
Chang Xu, Jinwang Wang, Wen Yang +3
Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on t…
Accurate Polygonal Mapping of Buildings in Satellite Imagery
Bowen Xu, Jiakun Xu, Nan Xue +1
This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and…
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
Chang Xu, Jinwang Wang, Wen Yang +3
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on…
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
Fang Xu, Yilei Shi, Patrick Ebel +4
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap betwee…
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
Qi Bi, Beichen Zhou, Kun Qin +2
Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly; 2) many objects irrelevant to the scene scheme are of…
Learning to Extract Building Footprints from Off-Nadir Aerial Images
Jinwang Wang, Lingxuan Meng, Weijia Li +3
Extracting building footprints from aerial images is essential for precise urban mapping with photogrammetric computer vision technologies. Existing approaches mainly assume that t…
An Empirical Study of Remote Sensing Pretraining
Di Wang, Jing Zhang, Bo Du +2
Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initiali…
Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration
Zi-Ming Wang, Nan Xue, Ling Lei +1
Given two point sets, the problem of registration is to recover a transformation that matches one set to the other. This task is challenging due to the presence of the large number…