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Shuai Chen

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12 papers

2 author rows

EAAI Journal 2026 Journal Article

Predicting inter-state cyberattacks with graph-text fusion using graph neural networks and large language models

Jiping Dong
Mengmeng Hao
Fangyu Ding
Shuai Chen
Jiajie Wu
Jun Zhuo

Accurate forecasting of inter-state cyberattacks is crucial for the timely prevention of security risks. However, the dynamic evolution of international relations and the heterogeneity of multi-source data make this task significant challenging in both data integration and model design. To address these issues, we propose GeoDyG-LLM (Geopolitical Dynamic Graph-Large Language Model), a unified multimodal framework for geopolitically grounded cyberattack prediction. The framework models inter-state interactions by integrating dynamic graph neural networks (GNNs) with large language models (LLMs), jointly capturing the temporal, structural, and semantic dependencies from historical cyberattack records and news events. Methodologically, the framework (i) employs a dynamic multi-view GNN with a learnable projector, whose node embeddings are layer-wise injected into the Transformer layers of the LLM; (ii) leverages LLM-based semantic refinement to construct a high-quality multimodal dataset from raw data sources; and (iii) adopts a geopolitically-aware negative sampling strategy to generate informative and balanced training pairs. Experimental results show that GeoDyG-LLM (8B) substantially outperforms baselines, achieving an F1 score of 0. 888 on cyberattack prediction. Moreover, the fine-tuned model generates coherent natural-language explanations aligned with historical and contextual evidence, enhancing interpretability and supporting practical geopolitical cyber risk analysis.

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NeurIPS Conference 2025 Conference Paper

Compress Large Language Models via Collaboration Between Learning and Matrix Approximation

Yuesen Liao
Zhiwei Li
Binrui Wu
Zihao Cheng
Su Zhao
Shuai Chen
Weizhong Zhang

Sparse and low-rank matrix composite approximation has emerged as a promising paradigm for compressing large language models (LLMs), offering a more flexible pruning structure than conventional methods based solely on sparse matrices. The significant variation in weight redundancy across layers, along with the differing rank and sparsity structures of weight matrices, makes identifying the globally optimal pruning structure extremely challenging. Existing methods often depend on uniform or manually designed heuristic rules to allocate weight sparsity across layers, subsequently compressing each matrix using matrix approximation techniques. Given the above theoretical difficulty in global compression of LLMs and the limited computational and data resources available compared to the training phase, we argue that a collaboration between learning and matrix approximation is essential for effective compression. In this paper, we propose a novel LLM compression framework based on generalized bilevel optimization that naturally formulates an effective collaborative mechanism. Specifically, the outer loop frames the weight allocation task as a probabilistic optimization problem, enabling the automatic learning of both layer-wise sparsities and matrix-wise retained ranks, while the inner loop solves the corresponding sparsity and rank-constrained model compression problem via matrix approximation. Our main technical contributions include two key innovations for efficiently solving this bilevel optimization problem. First, we introduce a truncated Gaussian prior-based probabilistic parameterization integrated with a policy gradient estimator, which avoids expensive backpropagation and stabilizes the optimization process. Second, we design an adapted QR-based matrix approximation algorithm that significantly accelerates inner loop computations. Extensive experiments on Phi-3 and the LLama-2/3 family demonstrate the effectiveness of our method. Notably, it maintains over 95\% zero-shot accuracy under 50\% sparsity and achieves up to 2× inference speedup.

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IJCAI Conference 2025 Conference Paper

CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection

Binjia Zhou
Hengrui Lou
Lizhe Chen
Haoyuan Li
Dawei Luo
Shuai Chen
Jie Lei
Zunlei Feng

With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection. Existing techniques for face forgery detection can broadly be categorized into two primary groups: visual-based methods and multimodal approaches. The former often lacks clear explanations for forgery details, while the latter, which merges visual and linguistic modalities, is more prone to the issue of hallucinations. To address these shortcomings, we introduce a visual detail enhanced self-correction framework, designated CorrDetail, for interpretable face forgery detection. CorrDetail is meticulously designed to rectify authentic forgery details when provided with error-guided questioning, with the aim of fostering the ability to uncover forgery details rather than yielding hallucinated responses. Additionally, to bolster the reliability of its findings, a visual fine-grained detail enhancement module is incorporated, supplying CorrDetail with more precise visual forgery details. Ultimately, a fusion decision strategy is devised to further augment the model's discriminative capacity in handling extreme samples, through the integration of visual information compensation and model bias reduction. Experimental results demonstrate that CorrDetail not only achieves state-of-the-art performance compared to the latest methodologies but also excels in accurately identifying forged details, all while exhibiting robust generalization capabilities.

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NeurIPS Conference 2025 Conference Paper

Efficient Representativeness-Aware Coreset Selection

Zihao Cheng
Binrui Wu
Zhiwei Li
Yuesen Liao
Su Zhao
Shuai Chen
Yuan Gao
Weizhong Zhang

Dynamic coreset selection is a promising approach for improving the training efficiency of deep neural networks by periodically selecting a small subset of the most representative or informative samples, thereby avoiding the need to train on the entire dataset. However, it remains inherently challenging due not only to the complex interdependencies among samples and the evolving nature of model training, but also to a critical coreset representativeness degradation issue identified and explored in-depth in this paper, that is, the representativeness or information content of the coreset degrades over time as training progresses. Therefore, we argue that, in addition to designing accurate selection rules, it is equally important to endow the algorithms with the ability to assess the quality of the current coreset. Such awareness enables timely re-selection, mitigating the risk of overfitting to stale subsets—a limitation often overlooked by existing methods. To this end, this paper proposes an E fficient R epresentativeness- A ware C oreset S election method for deep neural networks, a lightweight framework that enables dynamic tracking and maintenance of coreset quality during training. While the ideal criterion—gradient discrepancy between the coreset and the full dataset—is computationally prohibitive, we introduce a scalable surrogate based on the signal-to-noise ratio (SNR) of gradients within the coreset, which is the main technical contribution of this paper and is also supported by our theoretical analysis. Intuitively, a decline in SNR indicates overfitting to the subset and declining representativeness. Leveraging this observation, our method triggers coreset updates without requiring costly Hessian or full-batch gradient computations, maintaining minimal computational overhead. Experiments on multiple datasets confirm the effectiveness of our approach. Notably, compared with existing gradient-based dynamic coreset selection baselines, our method achieves up to a 5. 4\% improvement in test accuracy across multiple datasets.

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ICLR Conference 2025 Conference Paper

GS-CPR: Efficient Camera Pose Refinement via 3D Gaussian Splatting

Changkun Liu 0001
Shuai Chen
Yash Bhalgat
Siyan Hu
Ming Cheng
Zirui Wang
Victor Adrian Prisacariu
Tristan Braud

We leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement (CPR) framework, GS-CPR. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordinate regression methods. The 3DGS model renders high-quality synthetic images and depth maps to facilitate the establishment of 2D-3D correspondences. GS-CPR obviates the need for training feature extractors or descriptors by operating directly on RGB images, utilizing the 3D foundation model, MASt3R, for precise 2D matching. To improve the robustness of our model in challenging outdoor environments, we incorporate an exposure-adaptive module within the 3DGS framework. Consequently, GS-CPR enables efficient one-shot pose refinement given a single RGB query and a coarse initial pose estimation. Our proposed approach surpasses leading NeRF-based optimization methods in both accuracy and runtime across indoor and outdoor visual localization benchmarks, achieving new state-of-the-art accuracy on two indoor datasets.

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ICRA Conference 2024 Conference Paper

HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation

Changkun Liu 0001
Shuai Chen
Yukun Zhao
Huajian Huang
Victor Adrian Prisacariu
Tristan Braud

Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these unreliable predictions. However, existing uncertainty modelling techniques are often coupled with a specific APR architecture, resulting in suboptimal performance compared to state-of-the-art (SOTA) APR methods. This work introduces a novel APR-agnostic framework, HR-APR, that formulates uncertainty estimation as cosine similarity estimation between the query and database features. It does not rely on or affect APR network architecture, which is flexible and computationally efficient. In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR. The extensive experiments demonstrate the effectiveness of our framework, reducing 27. 4% and 15. 2% of computational overhead on the 7Scenes and Cambridge Landmarks datasets while maintaining the SOTA accuracy in single-image APRs.

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AAAI Conference 2023 Conference Paper

Dialogue Rewriting via Skeleton-Guided Generation

Chunlei Xin
Hongyu Lin
Shan Wu
Xianpei Han
Bo Chen
Wen Dai
Shuai Chen
Bin Wang

Dialogue rewriting aims to transform multi-turn, context-dependent dialogues into well-formed, context-independent text for most NLP systems. Previous dialogue rewriting benchmarks and systems assume a fluent and informative utterance to rewrite. Unfortunately, dialogue utterances from real-world systems are frequently noisy and with various kinds of errors that can make them almost uninformative. In this paper, we first present Real-world Dialogue Rewriting Corpus (RealDia), a new benchmark to evaluate how well current dialogue rewriting systems can deal with real-world noisy and uninformative dialogue utterances. RealDia contains annotated multi-turn dialogues from real scenes with ASR errors, spelling errors, redundancies and other noises that are ignored by previous dialogue rewriting benchmarks. We show that previous dialogue rewriting approaches are neither effective nor data-efficient to resolve RealDia. Then this paper presents Skeleton-Guided Rewriter (SGR), which can resolve the task of dialogue rewriting via a skeleton-guided generation paradigm. Experiments show that RealDia is a much more challenging benchmark for real-world dialogue rewriting, and SGR can effectively resolve the task and outperform previous approaches by a large margin.

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ECAI Conference 2023 Conference Paper

Instance-Wise Adaptive Tuning and Caching for Vision-Language Models

Chunjin Yang
Fanman Meng
Shuai Chen
Mingyu Liu
Runtong Zhang

Large-scale vision-language models (LVLMs) pre-trained on massive image-text pairs have achieved remarkable success in visual representations. However, existing paradigms to transfer LVLMs to downstream tasks encounter two primary challenges. Firstly, the text features remain fixed after being calculated and cannot be adjusted according to image features, which decreases the model’s adaptability. Secondly, the model’s output solely depends on the similarity between the text and image features, leading to excessive reliance on LVLMs. To address these two challenges, we introduce a novel two-branch model named the Instance-Wise Adaptive Tuning and Caching (ATC). Specifically, one branch implements our proposed ConditionNet, which guides image features to form an adaptive textual cache that adjusts based on image features, achieving instance-wise inference and improving the model’s adaptability. The other branch introduces the similarities between images and incorporates a learnable visual cache, designed to decouple new and previous knowledge, allowing the model to acquire new knowledge while preserving prior knowledge. The model’s output is jointly determined by the two branches, thus overcoming the limitations of existing methods that rely solely on LVLMs. Additionally, our method requires limited computing resources to tune parameters, yet outperforms existing methods on 11 benchmark datasets.

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EAAI Journal 2021 Journal Article

A population-based iterated greedy algorithm to minimize total flowtime for the distributed blocking flowshop scheduling problem

Shuai Chen
Quan-ke Pan
Liang Gao
Hong-yan Sang

We consider the distributed blocking flowshop scheduling problem (DBFSP) which is a meaningful generalization of the blocking flowshop scheduling problem in the distributed production environment. The objective of minimizing the total flowtime is relevant and important in the current dynamic manufacturing environment, but, as far as we know, it has not been investigated in the DBFSP previously. In this paper, a population-based iterated greedy (PBIG) algorithm is proposed to solve the DBFSP with the total flowtime criterion, which takes the advantage of both the population-based search approach and the iterated greedy algorithm. First, an effective constructive heuristic is proposed by integrating two existing constructive approaches to initialize the population with a high level of quality and diversity. Second, three different procedures to generate the offspring solutions are tested for the effective exploration capability, each of which rationally combines the destruction, reconstruction and selection operator. Third, the insertion neighborhood and swap neighborhood are investigated to enhance the local exploitation ability and a hybrid local search procedure that utilizes simultaneously both the two neighborhoods are proposed. The comprehensive experimental evaluation based on a total of 720 well-known instances shows that the proposed algorithms outperform the existing effective algorithms at a significant margin.

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AAAI Conference 2021 Conference Paper

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

Dongbo Xi
Bowen Song
Fuzhen Zhuang
Yongchun Zhu
Shuai Chen
Tianyi Zhang
Yuan Qi
Qing He

With the explosive growth of e-payment industry, online transaction fraud has become one of the biggest challenges for the business. The historical behavior information of users provides rich information for digging into the users’ fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit user’s behavioral information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields within one event have proven to be strong indicators of fraudulent behaviors. In this paper, we propose the Dual Importanceaware Factorization Machines (DIFM), which exploits the inter- and intra-event information among users’ behavior sequence from dual perspectives, i. e. , field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in Alipay’s risk management system, which provides real-time fraud detection service for ecommerce platforms. Experimental results on industrial data under various scenarios in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.

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IJCAI Conference 2019 Conference Paper

Automatic Grassland Degradation Estimation Using Deep Learning

Xiyu Yan
Yong Jiang
Shuai Chen
Zihao He
Chunmei Li
Shu-Tao Xia
Tao Dai
Shuo Dong

Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.

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TIST Journal 2018 Journal Article

D-Map+

Siming Chen
Shuai Chen
Zhenhuang Wang
Jie Liang
Yadong Wu
Xiaoru Yuan

Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user’s posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.

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