Author name cluster

Xiaoyun Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

ECAI Conference 2025 Conference Paper

A Multi-Degradation Dataset and a Universal Method for Image Deraining in All-Time Driving Scenes

Yiming Zheng
Yonghong Song
Xinyue Su
Xiaoyun Yang

Improving the visual quality of rainy driving scenes poses a significant challenge, as the rain streaks in the distance and the raindrops attached to nearby surfaces exhibit different characteristics under varying lighting conditions, both during the daytime and nighttime. We note that existing image deraining approaches are trained independently for specific types of rain degradation, which limits the model’s ability to adapt to dynamic driving scenes. In this paper, we introduce a new task: all-time rainy driving scene reconstruction, which aims to simultaneously address both daytime and nighttime rain degradation using a universal mix-trained model. Firstly, we construct a high-quality benchmark dataset termed RainDrive-10K, which contains four patterns: daytime rain streak, daytime raindrop, nighttime rain streak and nighttime raindrop. Furthermore, we also develop an effective Mamba-based baseline de-raining model, which employs a multi-patch progressive learning strategy to better help image restoration. Unlike existing Mamba-based methods that use fixed-scale scanning for feature extraction, we design a new multi-patch hierarchical scanning block that improves the model’s robustness to diverse rain appearances. Extensive experiments demonstrate the effectiveness of our proposed model, and show that it achieves favorable performance against state-of-the-art ones. The dataset is available at https: //github. com/ZXXaaaa/MP-RainMamba.

Details

AAAI Conference 2024 Conference Paper

Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition

Jianyang Xie
Yanda Meng
Yitian Zhao
Anh Nguyen
Xiaoyun Yang
Yalin Zheng

Graph convolutional networks (GCNs) have attracted great attention and achieved remarkable performance in skeleton-based action recognition. However, most of the previous works are designed to refine skeleton topology without considering the types of different joints and edges, making them infeasible to represent the semantic information. In this paper, we proposed a dynamic semantic-based graph convolution network (DS-GCN) for skeleton-based human action recognition, where the joints and edge types were encoded in the skeleton topology in an implicit way. Specifically, two semantic modules, the joints type-aware adaptive topology and the edge type-aware adaptive topology, were proposed. Combining proposed semantics modules with temporal convolution, a powerful framework named DS-GCN was developed for skeleton-based action recognition. Extensive experiments in two datasets, NTU-RGB+D and Kinetics-400 show that the proposed semantic modules were generalized enough to be utilized in various backbones for boosting recognition accuracy. Meanwhile, the proposed DS-GCN notably outperformed state-of-the-art methods. The code is released here https://github.com/davelailai/DS-GCN

PDF Details DOI

JBHI Journal 2022 Journal Article

Semi-Supervised Learning for Automatic Atrial Fibrillation Detection in 24-Hour Holter Monitoring

Peng Zhang
Yuting Chen
Fan Lin
Sifan Wu
Xiaoyun Yang
Qiang Li

Paroxysmal atrial fibrillation (AF) is generally diagnosed by long-term dynamic electrocardiogram (ECG) monitoring. Identifying AF episodes from long-term ECG data can place a heavy burden on clinicians. Many machine-learning-based automatic AF detection methods have been proposed to solve this issue. However, these methods require numerous annotated data to train the model, and the annotation of AF in long-term ECG is extremely time-consuming. Reducing the demand for labeled data can effectively improve the clinical practicability of automatic AF detection methods. In this study, we developed a novel semi-supervised learning method that generated modified low-entropy labels of unlabeled samples for training a deep learning model to automatically detect paroxysmal AF in 24 h Holter monitoring data. Our method employed a 1D CNN-LSTM neural network with RR intervals as input and used few labeled training data with numerous unlabeled data for training the neural network. This method was evaluated using a 24 h Holter monitoring dataset collected from 1000 paroxysmal AF patients. Using labeled samples from only 10 patients for model training, our method achieved a sensitivity of 97. 8%, specificity of 97. 9%, and accuracy of 97. 9% in five-fold cross-validation. Compared to the supervised learning method with complete labeled samples, the detection accuracy of our method was only 0. 5% lower, while the workload of data annotation was significantly reduced by more than 98%. In general, this is the first study to apply semi-supervised learning techniques for automatic AF detection using ECG. Our method can effectively reduce the demand for AF data annotations and can improve the clinical practicability of automatic AF detection.

Details DOI