57 citations · 137 across the 28 of their papers we have counts for
27 papers · 1 filter
BladeYOLO: Wind Turbine Blade Defect Detection with Limited Annotations and Weak-Saliency Awareness
Yabin Xu, Fangtao Zhang, Fan Wang +5
Wind turbine blade defect detection remains highly challenging in real-world inspection scenarios due to limited on-site data and the subtle visual characteristics of defects. In p…
AlignFreeNet: Is Cross-Modal Pre-Alignment Necessary? An End-to-End Alignment-Free Lightweight Network for Visible-Infrared Object Detection
Dingkun Zhu, Haote Zhang, Lipeng Gu +7
Cross-modal misalignments, such as spatial offsets, resolution discrepancies, and semantic deficiencies, frequently occur in visible-infrared object detection (VI-OD). To mitigate…
Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features
Wuzhou Quan, Wei Zhao, Weiming Wang +3
Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local…
FriendNet: Detection-Friendly Dehazing Network
Yihua Fan, Yongzhen Wang, Mingqiang Wei +2
Adverse weather conditions often impair the quality of captured images, inevitably inducing cutting-edge object detection models for advanced driver assistance systems (ADAS) and a…
Cross-BERT for Point Cloud Pretraining
Xin Li, Peng Li, Zeyong Wei +7
Introducing BERT into cross-modal settings raises difficulties in its optimization for handling multiple modalities. Both the BERT architecture and training objective need to be ad…
TransFace++: Rethinking the Face Recognition Paradigm with a Focus on Accuracy, Efficiency, and Security
Jun Dan, Yang Liu, Baigui Sun +2
Face Recognition (FR) technology has made significant strides with the emergence of deep learning. Typically, most existing FR models are built upon Convolutional Neural Networks (…
Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval
Mingqiang Wei, Qian Sun, Haoran Xie +2
Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e.g., background, pose, visual angle, brightness) of real-world…
Rethinking Real-world Image Deraining via An Unpaired Degradation-Conditioned Diffusion Model
Yiyang Shen, Mingqiang Wei, Yongzhen Wang +2
Recent diffusion models have exhibited great potential in generative modeling tasks. Part of their success can be attributed to the ability of training stable on huge sets of paire…
Rethinking Generative Methods for Image Restoration in Physics-based Vision: A Theoretical Analysis from the Perspective of Information
Xudong Kang, Haoran Xie, Man-Leung Wong +1
End-to-end generative methods are considered a more promising solution for image restoration in physics-based vision compared with the traditional deconstructive methods based on h…
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
Yiyang Shen, Rongwei Yu, Peng Wu +4
LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing method…
PointSee: Image Enhances Point Cloud
Lipeng Gu, Xuefeng Yan, Peng Cui +5
There is a trend to fuse multi-modal information for 3D object detection (3OD). However, the challenging problems of low lightweightness, poor flexibility of plug-and-play, and ina…
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
Tianyu Zhang, Xusheng Du, Chia-Ming Chang +2
Scene understanding is an essential and challenging task in computer vision. To provide the visually fundamental graphical structure of an image, the scene graph has received incre…
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
Zhaowei Chen, Peng Li, Zeyong Wei +4
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully exploit the geometric inf…
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
Zhiheng Hu, Yongzhen Wang, Peng Li +3
Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in…
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Yongzhen Wang, Xuefeng Yan, Kaiwen Zhang +4
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in the…
Contrastive Semantic-Guided Image Smoothing Network
Jie Wang, Yongzhen Wang, Yidan Feng +5
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in…
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Baian Chen, Liangliang Nan, Haoran Xie +3
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, eith…
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Peng Wu, Lipeng Gu, Xuefeng Yan +4
Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing…
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
Zhe Zhu, Liangliang Nan, Haoran Xie +4
How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, a…
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Chen Chen, Yisen Wang, Honghua Chen +6
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most…
Dynamic Message Propagation Network for RGB-D Salient Object Detection
Baian Chen, Zhilei Chen, Xiaowei Hu +4
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature…
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
Qiqi Ding, Peng Li, Xuefeng Yan +6
Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also…
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
Yongzhen Wang, Xuefeng Yan, Fu Lee Wang +4
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known d…
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Yiyang Shen, Yongzhen Wang, Mingqiang Wei +4
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain…