Xiangfeng Luo

AAAI Conference 2025 Conference Paper

Context-aware Graph Neural Network for Graph-based Fraud Detection with Extremely Limited Labels

• Pengbo Li
• Hang Yu
• Xiangfeng Luo

Graph-based fraud detection is crucial in identifying illegal activities in social networks, finance, and other sectors. Despite recent progress in this area, most of current researches typically require a large amount of annotated data to demonstrate its benefits. In practice, obtaining sufficient high-quality annotated data is challenging, limiting the effectiveness of model training. Therefore, leveraging extremely limited label information is crucial to enhance model performance. We propose a context-aware graph neural network (CGNN) to address this. CGNN performs category semantic decomposition on the contextual neighbor features of the center node to enrich the category semantics. In the neighbor message aggregation stage, the denoising attention mechanism enables the center node to adaptively aggregate heterophilic and homophilic information from neighbors. Particularly for unlabeled data, feature augmentation within the category subspace and consistency regularization driven by entropy minimization ensure that such data can further enhance model performance under explicit semantic guidance. We demonstrate on four real-world datasets that CGNN significantly outperforms other baseline methods with extremely limited labels.

EAAI Journal 2025 Journal Article

Saliency and correlation learning for co-salient object detection

• Ying Tong
• Xiangfeng Luo
• Liyan Ma
• Shaorong Xie

Co-Salient object detection aims to identify common salient objects across a given group of images. However, accurately locating co-salient objects remains challenging due to the complexity of capturing the correlation representation of each group of images. To tackle this problem, we propose a saliency and correlation learning method for co-salient object detection. This method employs a saliency learning network and a correlation learning network to generate precise co-saliency maps of a group of images. Within the saliency learning network, a saliency feature grafting module is designed to refine object edges and achieve accurate detection of salient objects. Furthermore, the correlation learning network incorporates two modules, which are designed for extracting saliency correlation representation and deriving consensus correlation representation within a group of images, respectively. Guided by prior information obtained from saliency learning of images, our method significantly improves performance in co-salient object detection through correlation representation learning. Extensive experiments on all the latest benchmarks demonstrate that our method outperforms 11 state-of-the-art models, achieving a new level of technical excellence, with an average Structural Similarity Measure score of 0. 845.

EAAI Journal 2025 Journal Article

Spatial–temporal intention representation with multi-agent reinforcement learning for unmanned surface vehicles strategies learning in asset guarding task

• Yang Li
• Shaorong Xie
• Hang Yu
• Han Zhang
• Zhenyu Zhang
• Xiangfeng Luo

As a typical application of artificial intelligence, autonomous and intelligent Unmanned Surface Vehicles (USVs) hold significant practical value in asset guarding tasks, as they can ensure target security while significantly reducing costs. Each USV is tasked with inferring adversary intentions based on locally observable information and proactively intercepting intruding boats to maximize asset survival time. Most current methods primarily identify and represent intentions through either prior rule matching or low-dimensional behavioral features. However, rule-based methods struggle to handle dynamically changing intents, and behavioral features can easily introduce uncertainty in intention recognition. In this paper, we propose a Spatial–Temporal Intention Representation (STIR) model that effectively conveys the dynamic intentions of various boats in asset guarding tasks, enhancing the learning efficiency of USVs’ strategies. First, utilizing local observation information from USVs, we construct an intention recognition tree that correlates intentions with a priori task background knowledge, thereby reducing uncertainty in intention recognition. Second, we develop a spatial–temporal attention network to dynamically represent intentions in both spatial and temporal dimensions, improving the USVs’ understanding of local scene dynamics. Third, we combine STIR with multi-agent reinforcement learning to train the interception strategies of USVs. In the experiments, we discuss the positive impact of STIR on adversarial strategy learning in the asset guarding task. Simulation results illustrate the advantages of our approach in terms of learning speed and strategy effectiveness.

AAAI Conference 2024 Conference Paper

Barely Supervised Learning for Graph-Based Fraud Detection

• Hang Yu
• Zhengyang Liu
• Xiangfeng Luo

In recent years, graph-based fraud detection methods have garnered increasing attention for their superior ability to tackle the issue of camouflage in fraudulent scenarios. However, these methods often rely on a substantial proportion of samples as the training set, disregarding the reality of scarce annotated samples in real-life scenarios. As a theoretical framework within semi-supervised learning, the principle of consistency regularization posits that unlabeled samples should be classified into the same category as their own perturbations. Inspired by this principle, this study incorporates unlabeled samples as an auxiliary during model training, designing a novel barely supervised learning method to address the challenge of limited annotated samples in fraud detection. Specifically, to tackle the issue of camouflage in fraudulent scenarios, we employ disentangled representation learning based on edge information for a small subset of annotated nodes. This approach partitions node features into three distinct components representing different connected edges, providing a foundation for the subsequent augmentation of unlabeled samples. For the unlabeled nodes used in auxiliary training, we apply both strong and weak augmentation and design regularization losses to enhance the detection performance of the model in the context of extremely limited labeled samples. Across five publicly available datasets, the proposed model showcases its superior detection capability over baseline models.

EAAI Journal 2024 Journal Article

Hierarchical visual semantic guidance for enhanced relationship recognition in domain knowledge graphs

• Xinzhi Wang
• Jiayu Guo
• Xiangfeng Luo

Multi-modal entity relationship recognition is a crucial foundation for constructing accurate and comprehensive domain knowledge graphs. However, due to the sparse and diverse nature of textual semantic information and the semantic differences exhibited by multi-modal data, existing methods often suffer from issues such as information loss and noise features. In this paper, the Hierarchical Visual Semantic Guidance (HVSG) Network is proposed, which utilizes images to furnish visual semantic information for text, facilitating the identification of implicit relationships between entities. Specifically, salient local instance objects are initially extracted from the global image, followed by the use of a hierarchical visual semantic construction module to establish multi-level visual semantics. The integration of multi-level visual and textual features is then achieved through a visual semantic guidance module. This work constructed a Chinese multi-modal entity relation classification dataset in the domain of unmanned vessels and conducted experiments on two datasets. Compared to state-of-the-art (SOTA) models, HVSG achieved the best performance, with F1 scores improved by 1. 05% and 0. 58% respectively, indicating that HVSG can offer better performance in exploring implicit relationships between textual entities.

EAAI Journal 2023 Journal Article

THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection

• Yinsai Guo
• Hang Yu
• Liyan Ma
• Liang Zeng
• Xiangfeng Luo

In boat navigation, especially in complex sea conditions, the detection performance of the tiny boat is related to the safety of boat sailing. However, due to the tiny boat occupying fewer pixels, the effective features of the tiny boat are difficult to obtain. Current tiny object detection methods focus primarily on dataset size matching, feature fusion, and label assignment, lack of attention to texture and detail information loss, and insufficient semantic utilization. Here, we propose a Triple-hierarchy Feature Enhancement (THFE) method to detect tiny boats. The core idea behind THFE is to enhance the semantic information from different layers to supplement the effective features of tiny boats. It consists of three spaces: the super-resolution enhancement space, the semantic enhancement space, and the hierarchical enhancement space. In THFE, sub-pixel convolution, sparse self-attention mechanism, channel attention mechanism, and spatial attention mechanism are adopted to hierarchically enhance each layer’s high-level and low-level semantic features. Finally, each layer’s high-level and low-level semantic features are adaptively fused so that each feature map contains richer high-level and low-level semantic information. Experiments show that our proposed THFE method achieves impressive gains in detection performance. For example, in terms of A P 50 t i n y, our method outperforms state-of-the-art methods by 1. 7 % on the TinyBoats dataset, 3. 1 % on the TinyPersons Dataset and 3. 9 % on the Tiny CityPersons Dataset. To further study the detection of tiny boats, we introduce a tiny boat dataset that will be publicly accessible.

TIST Journal 2013 Journal Article

Introduction to the special section on intelligent tutoring and coaching systems

• Qing Li
• Xiangfeng Luo
• Liu Wenyin
• Cristina Conati