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arXiv 2025年

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Robustifying Vision Transformer for Image Forgery Localization with Multi-Exit Architectures

SSRN

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SSRN 2023年

作者： Shi, Zenan Chen, Haipeng Dong, Zhang College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

The proliferation of image manipulation tools has led to an increase in the number of manipulated images being disseminated online, posing risks like the propagation of fake news and telecom fraud. Thus, there is an increasing demand for precise, generic, and robust methods for detecting and locating manipulated images. In this paper, we propose a simple and clean model, named MEAFormer, for image forgery localization that does not heavily rely on pre-training models. MEAFormer comprises three main components: encoder network, neck network, and decoder network. Specifically, the transformer-based encoder network extracts hierarchical feature representations from the input image, providing rich contextual information in each layer. The neck network, incorporating our proposed cross-layer feature aggregation (CFA), aggregates these hierarchical features. To achieve better spatial feature co-occurrence, instead of using noise or edge artifacts, we introduce a multi-scale graph reasoning (MGR) module within the decoder network via bipartite graphs over the encoder and decoder features in a multi-scale fashion. The cross-level enhancement (CLE) further performs adjacent-level feature fusion to amplify the region-of-interests in aggregated manipulation features. Finally, the multi-exit architecture (MEA) guides the model to learn fine-grained features and segment out the manipulated region. Extensive experiments across diverse and challenging datasets conclusively establish MEAFormer’s superiority over existing state-of-the-art methods, excelling in accuracy, generalization, and robustness. © 2023, The Authors. All rights reserved.

关键词： Network architecture

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HVIS: A Human-like Vision and Inference System for Human Motion Prediction

arXiv

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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

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Dual Personalization on Federated Recommendation

arXiv

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arXiv 2023年

作者： Zhang, Chunxu Long, Guodong Zhou, Tianyi Yan, Peng Zhang, Zijian Zhang, Chengqi Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Computer Science and Technology Jilin University China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Australia Computer Science and UMIACS University of Maryland United States

Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Short Tail:taming tail latency for erasure-code-based in-memory systems

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Frontiers of Information Technology & Electronic engineering 2022年第11期23卷 1646-1657页

作者： Yun TENG Zhiyue LI Jing HUANG Guangyan ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China Beijing National Research Center for Information Science and Technology(Tsinghua University) Beijing 100084China

In-memory systems with erasure coding(EC)enabled are widely used to achieve high performance and data ***,as the scale of clusters grows,the server-level fail-slow problem is becoming increasingly frequent,which can create long tail *** influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC *** this paper,we propose an EC-enabled in-memory storage system called ShortTail,which can achieve consistent performance and low latency for both reads and ***,ShortTail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow ***,ShortTail selectively performs degraded reads and redirected writes to avoid accessing fail-slow ***,ShortTail posts an adaptive write strategy to reduce write amplification of small *** implement ShortTail on top of Memcached and compare it with two baseline *** experimental results show that ShortTail can reduce the P99 tail latency by up to 63.77%;it also brings significant improvements in the median latency and average latency.

关键词： Erasure code In-memory system Node fail-slow Small write Tail latency

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Deep Feature-Based Text Clustering and Its Explanation

Deep Feature-Based Text Clustering and Its Explanation

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International Conference on Data engineering

作者： Renchu Guan Hao Zhang Yanchun Liang Fausto Giunchiglia Lan Huang Xiaoyue Feng Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China DISI University of Trento Trento Italy

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering.

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Community discovery algorithm and its technical improvement based on link structure–taking web community algorithm as an example

Community discovery algorithm and its technical improvement ...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Gao, Rui Yang, Wenzhe Shi, Xiaohu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China

ISBN: (纸本)9783030539795

Link analysis is an important way to discover potential web communities. This paper analyzes the characteristics of the Web link structure, and studies the traditional maximum flow algorithm and the maximum flow algorithm based on HITS algorithm for edge capacity allocation. The traditional maximum flow problem, but there are still shortcomings. Based on the analysis of existing link similarity definitions, this paper proposes a new definition of link similarity and topic dissimilarity to better describe the relationship between linked pages, and to measure based on link similarity and topic dissimilarity. The similarity of the pages gives a more reasonable and efficient maximum flow-side capacity allocation scheme. The community discovery of 6 topics shows that the maximum flow algorithm proposed in this paper using the link similarity and topic dissimilarity model to allocate edge capacity can better solve the problems of existing algorithms and significantly improve the quality of the Web community. This paper proposes new methods and ideas for page similarity measurement, and provides new strategies for improving the Web community discovery algorithm based on link analysis. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Computer programming

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Convergence-Aware Optimal Checkpointing for Exploratory Deep Learning Training Jobs

SSRN

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SSRN 2024年

作者： Li, Hongliang Wang, Zichen Zhao, Hairui Zhang, Meng Li, Xiang Xu, Haixiao College of Computer Science and Technology Jilin University Qianjin street 2699 Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Qianjin street 2699 Jilin Changchun130012 China High Performance Computing Center Qianjin street 2699 Jilin Changchun130012 China

Training Deep Learning (DL) models are becoming more time-consuming, thus interruptions to the training processes are inevitable. Existing fault-tolerant work adopted checkpoint/recovery mechanism from traditional HPC platforms that makes periodical persistent copies of model states to save execution progress. These checkpoints have fixed time intervals that they are evenly placed across the training. We can obtain an optimal checkpointing interval for an HPC job with the precondition that the progress of a job is proportional to its execution time. Unfortunately, it is not the case in DL model training where a DL training job yields diminishing returns across its lifetime. It makes the early progress of a DL training job more valuable than the later ones. Even placement of checkpoints would either increase the risks in the early stages or waste resources overprotecting the latter stages. Meanwhile, the issue can get amplified for exploratory training jobs, where early terminations are common. Moreover, in data parallelism, the state-of-art quality-driven scheduling strategies allocate more resources for the early stages of a job than the later ones to accelerate the training progress which further amplifies the issue. This paper introduces a novel checkpointing interval problem for exploratory DL training jobs based on model convergence progress. We present COCI, an approach to compute optimal checkpointing configuration for a DL training job, minimizing the fault-tolerant overhead, including checkpointing cost and recovery cost. We implement COCI based on state-of-art iteration-level checkpointing mechanism, as a pluggable module compatible with PyTorch. COCI requires no extra user input. We conduct comprehensive evaluations with real DL application setups. The experimental results show that COCI reduces up to 40.18% fault-tolerant overhead compared to existing state-of-the-art DL fault-tolerant methods in serial scenario, 57.26% in data parallel scenario and 63.8

关键词： Iterative methods

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An Adversarial Complementary Erasing Network for Salient Object Detection in Optical Remote Sensing Images

SSRN

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SSRN 2023年

作者： Liang, Yanhua Qin, Guihe Sun, Minghui Qin, Jun Yan, Jie Zhang, Zhonghan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130012 China

Salient object detection in optical remote sensing images (RSI-SOD) aims to locate and extract visually discriminative objects or regions, which has attracted more and more attention from the community. The key challenge of RSI-SOD lies in the characteristics of optical RSIs, such as scale diversification of salient objects, irregular edges, and cluttered backgrounds. In this paper, we strive to embrace challenges towards effective and efficient RSI-SOD. To this end, we develop a framework, termed Adversarial Complementary Erasing Network (ACENet), which comprehensively mines discriminative features of different layers from foreground, background, local and global perspectives to perform robust and accurate saliency detection. Specifically, ACENet is a typical encoder-decoder structure that embeds Adversarial Erasing Synthesis Module (AESM) in multi-scale encoding features to fully explore more useful and complete representations. In AESM, we introduce Adversarial Discriminant Information Mining (ADIM) component to facilitate adaptive adjustment between salient features and background, further enabling more representative and strong integrity feature generation. Experimental results on three datasets prove that our ACENet significantly outperforms cutting-edge SOD models both qualitatively and quantitatively with a quick reasoning speed of 76 FPS. Code will be available at https://***/Liangyh18/ACENet. © 2023, The Authors. All rights reserved.

关键词： Optical remote sensing

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Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

arXiv

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arXiv 2024年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Hong Kong University 999077 Hong Kong

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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