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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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OpenViewer: Openness-Aware Multi-View Learning 39

OpenViewer: Openness-Aware Multi-View Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)157735897X

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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Object Detection Algorithm Based on Second-Order Pooling network and Gaussian Mixture Attention 5

Object Detection Algorithm Based on Second-Order Pooling Net...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Li, Ningbo Yang, Xiaobao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To improve the feature representation ability of the YOLOX algorithm and obtain better detection performance, an object detection algorithm based on second-order pooling network and gaussian mixture attention is proposed. Firstly, the second-order pooling network is added after the PAFPN, and the higher-order statistical information is obtained by calculating the covariance matrix between different channels, to enhance the non-linear modeling capability. Secondly, the mixture attention based on the gaussian function is added after the SPP to model global contexts in the spatial and channel dimensions respectively, which improves the network performance with almost no extra parameters. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 82.6 %, which is 1.6 % higher than the YOLOX algorithm. © 2022 ACM.

关键词： Object detection

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Symmetry-induced fragmentation and dissipative time crystal

arXiv

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arXiv 2025年

作者： Li, Haowei Yi, Wei CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China Hefei230026 China CAS Center For Excellence in Quantum Information and Quantum Physics Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China 5Anhui Center for Fundamental Sciences in Theoretical Physics University of Science and Technology of China Hefei230026 China

We propose a general protocol for engineering dissipative time crystals in quantum open systems through symmetry-induced fragmentation and ergodicity breaking. Building upon a U(1)-symmetry-induced Liouville-space fragmentation, we devise a generic Liouvillian possessing a series of non-dissipative eigenmodes with purely imaginary eigenvalues, which give rise to long-time oscillations breaking both ergodicity and time-translation symmetry. Illustrating our proposal using a dissipative lattice model, we show that, even when the U(1) symmetry is broken, a prethermal time-crystal behavior survives, with distinct oscillation frequencies at different times of the steady-state approaching dynamics. The richly structured prethermal time-crystal dynamics derive from Fermi statistics and the Liouvillian skin effect of our model, under which excitations above the boundary-localized dark states can be mapped to the irreducible representations of the permutation group. The branching rules of the permutation group then dictate that the Liouvillian gaps acquired by the non-dissipative eigenmodes should be higher-order powers of the symmetry-breaking perturbation, leading to the prethermal time-crystal dynamics. Copyright © 2025, The Authors. All rights reserved.

关键词： Crystal symmetry

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UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism 5

UAV Visual Tracking Algorithm Based on Feature Fusion of the...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Zhang, Zixian Zhao, Zhixian Yang, Xiaobao Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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D4M: dataset Distillation via Disentangled Diffusion Model

arXiv

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arXiv 2024年

作者： Su, Duo Hou, Junjie Gao, Weizhi Tian, Yingjie Tang, Bowen School of Computer Science and Technology UCAS United Kingdom Sino-Danish College UCAS United Kingdom Department of Computer Science NCSU United States School of Economics and Management UCAS United Kingdom Research Center on Fictitious Economy and Data Science CAS China Key Laboratory of Big Data Mining and Knowledge Management CAS China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS United Kingdom Institute of Computing Technology CAS China

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D4M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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network Situation Awareness Model Based on Incomplete Information Game 5th

Network Situation Awareness Model Based on Incomplete Inform...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： network security

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Results of 23 yr of Pulsar Timing of PSR J1453-6413

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Research in Astronomy and Astrophysics 2023年第10期23卷 357-366页

作者： Wei Li Shi-Jun Dang Jian-Ping Yuan Lin Li Wei-Hua Wang Lun-Hua Shang Na Wang Qing-Ying Li Ji-Guang Lu Fei-Fei Kou Shuang-Qiang Wang Shuo Xiao Qi-Jun Zhi Yu-Lan Liu Ru-Shuang Zhao Ai-Jun Dong Bin Zhang Zi-Yi You Yan-Qing Cai Ya-Qin Yang Ying-Ying Ren Yu-Jia Liu Heng Xu School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550001China Guizhou Radio Astronomy Observatory Guizhou UniversityGuiyang 550025China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China School of Physics Science and Technology Xinjiang UniversityUrumqi 830046China College of Mathematics and Physics Wenzhou UniversityWenzhou 325035China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China

In this paper,we presented the 23.3 yr of pulsar timing results of PSR J1456-6413 based on the observations of Parkes 64 m radio *** detected two new glitches at MJD 57093(3)and 59060(12)and confirmed its first glitch at MJD 54554(10).The relative sizes(Δν/ν)of these two new glitches are 0.9×10^(-9)and 1.16×10^(-9),*** the“Cholesky”timing analysis method,we have determined its position,proper motion,and two-dimensional transverse velocities from the data segments before and after the second glitch,***,we detected exponential recovery behavior after the first glitch,with a recovery timescale of approximately 200 days and a corresponding exponential recovery factor Q of approximately 0.15(2),while no exponential recovery was detected for the other two *** interestingly,we found that the leading component of the integral pulse profile after the second glitch became stronger,while the main component became *** results will expand the sample of pulsars with magnetosphere fluctuation triggered by the glitch event.

关键词： Stars (stars:)pulsars:individual(PSR J1453-6413) stars:neutron

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Not all diffusion model activations have been evaluated as discriminative features 24

Not all diffusion model activations have been evaluated as d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at https://***/Darkbblue/generic-diffusion-feature.

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Blind separation of noncooperative paired carrier multiple access signals based on improved quantum-inspired evolutionary algorithm and receding horizon optimization

Blind separation of noncooperative paired carrier multiple a...

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作者： Deng, Qi Zhang, Shanshan Chen, Gang Lu, Huaxiang Lab of Artificial Networks Institute of Semiconductors CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

The single-channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high-order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel estimation and sequence detection, and a novel blind separation algorithm based on the improved quantum-inspired evolutionary algorithm (IQEA) and receding horizon optimization was proposed. Considering the practical scenarios, a mixed PCMA signal model with noninteger period sampling was presented in advance. Combined with the proposed signal model, the IQEA was applied to recover the uplink signals from the mixed PCMA signals by its iterative estimation. Meanwhile, to reduce the computational complexity caused by long memory of PCMA signals, the receding horizon optimization was introduced to process the whole signal sequence as a dynamic programming problem. The simulation results verified that the proposed RH-IQEA outperforms existing state-of-the-art separation algorithms in the blind separation of noncooperative PCMA signals, with higher accuracy, lower-order complexity and stronger robustness to parameter estimation errors. © 2021 John Wiley & Sons, Ltd.

关键词： Iterative methods

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