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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Li, Ruilong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781510681880

With the increasing demands for real-time accuracy in visual SLAM (Simultaneous Localization and Mapping), the critical front-end component of SLAM systems has become the extraction and matching of image features to estimate camera motion pose. Existing feature extraction methods are susceptible to local feature point density and even overlap due to uncertainties in lighting intensity and surrounding environments, leading to uneven distribution of overall feature points. This, in turn, affects the accuracy of feature matching, thereby impacting the performance of the SLAM process. To address these challenges, this paper proposes a novel ORB image feature extraction algorithm based on fuzzy control. Initially, a Gaussian pyramid of the image is constructed, and the Intensity Centroid is calculated to ensure scale and rotation invariance. Subsequently, candidate feature points are extracted using ORB at each pyramid level, then the candidate feature points are selectively filtered through fuzzy control based on the number of candidate feature points and the magnitude of their response values. Finally, a BRIEF descriptor is established for feature matching. The proposed Fuzzy-Controlled ORB Feature Extraction (OFFC) algorithm was experimentally validated on open-source TUM and Mikolajczyk datasets. Results indicate that OFFC successfully avoids excessive density and overlap of extracted feature points, reduces mismatches in image feature points, and enhances the accuracy of feature matching. Notably, OFFC demonstrates adaptability and maintains robust performance in three challenging scenarios: image blurring, varying brightness, and dense similarity of image blocks, highlighting its significant practical value for visual SLAM. © 2024 SPIE.

关键词： Fuzzy control

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Double-Mix-Net: A Multimodal Music Emotion Recognition Network with Multi-layer Feature Mixing 10th

Double-Mix-Net: A Multimodal Music Emotion Recognition Netwo...

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10th Conference on Sound and Music technology, CSMT 2023

作者： Li, Peilin Chen, Kairan Wei, Weixin Zhao, Jiahao Li, Wei School of Computer Science and Technology Fudan University Shanghai Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai Shanghai200433 China

ISBN: (纸本)9789819779611

Computational music emotion recognition (MER) is an important task that aims to recognize emotional content in music tracks. Understanding the emotional content of music can help tailor therapeutic interventions to specific emotional needs, potentially improving their effectiveness. Piano music is widely used in music therapy for depression, anxiety, and other mental health conditions. However, comparatively little work has been done on MER in piano music, which is a challenging task due to the unique difficulties in recognizing emotions in single-timbre music without vocals, especially on valence. In this paper, we propose a novel Double-Mix-Net model that utilizes the multi-layer feature mixing and multi-modal feature mixing methods to improve classification performance. Our model uses both the title of the YouTube music video and the music context to predict the emotion of a given music track. Additionally, we make full use of different granularity features through the multi-layer feature mixing method. Our proposed model achieved state-of-the-art accuracy performance of 73.8% on the Emopia dataset and is the first multi-modal network designed for piano emotion recognition. The results of our study highlight the importance of considering the music title and utilizing multi-layer feature mixing methods in piano music emotion recognition. This work helps the emotional understanding of piano music. Our proposed model can be applied in various domains such as music recommendation systems, film scoring, and game design to enhance the emotional impact of music. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Game design

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Ranking the Spreading Influence of Nodes in Complex Networks by Combining Local Average Weight and Average Normalized Link Entropy 4

Ranking the Spreading Influence of Nodes in Complex Networks...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Fan, Kaiyu Yang, Fan Tu, Dexin Tang, Xinlai Mao, Xiuyong Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Guangxi University of Science and Technology Key Laboratory of Intelligent Information Processing and Graph Processing Liuzhou545006 China

ISBN: (纸本)9798350343618

Ranking the spreading influence of nodes is of great importance for controlling the spreading process on networks. Although a few measures are proposed for ranking nodes in weighted networks, it is still a challenge in accurately and efficiently ranking the spreading influence of nodes in weighted networks. In this paper, by utilizing both of the local and global topological structure information, a novel method is proposed. First, we define a local average weight (LAW) to describe the local topological structure information of nodes. Second, partly based on the link diversity, we define an average normalized link entropy (ANLE) to describe the global topological structure information of nodes. Further, by combining the LAW and ANLE, we propose a novel ranking measure termed as LAW-ANLE (LA) centrality. The proposed LA is verified by extensive experiments on a series of networks, and the results show that the LA is feasible and effective in accurately ranking the spreading influence of nodes. © 2023 IEEE.

关键词： Complex networks

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LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising

LED: A Large-scale Real-world Paired Dataset for Event Camer...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuxing Duan National Key Lab of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Event camera has significant advantages in capturing dynamic scene information while being prone to noise in-terference, particularly in challenging conditions like low threshold and low illumination. However, most existing research focuses on gentle situations, hindering event camera applications in realistic complex scenarios. To tackle this limitation and advance the field, we construct a new paired real-world event denoising dataset (LED), including 3K sequences with 18K seconds of high-resolution (1200*680) event streams and showing three notable distinctions compared to others: diverse noise levels and scenes, larger-scale with high-resolution, and high-quality GT. Specifically, it contains stepped parameters and varying illumination with diverse scenarios. Moreover, based on the property of noise events inconsistency and signal events consistency, we propose a novel effective denoising frame-work(DED) using homogeneous dual events to generate the GT with better separating noise from the raw. Further-more, we design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholds to realize accurate denoising. The experimental results demonstrate that the remarkable performance of the proposed approach on different datasets. The dataset and code are at https://***/Yee-Sing/led.

关键词： Computer vision Noise reduction Noise Neurons Lighting Light emitting diodes Cameras

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Gaitcotr: Improved Spatial-Temporal Representation for Gait Recognition with a Hybrid Convolution-Transformer Framework 48

Gaitcotr: Improved Spatial-Temporal Representation for Gait ...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Jingqi Zhang, Yuzhen Shan, Hongming Zhang, Junping School of Computer Science Shanghai Key Lab of Intelligent Information Processing China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai200433 China

ISBN: (纸本)9781728163277

This work presents a novel hybrid convolution-transformer framework for gait recognition, termed GaitCoTr. The developed framework captures the appearance and short-term temporal features by convolution and extracts the long-term temporal features by transformer architecture, achieving a comprehensive spatial-temporal representation of gait. To unleash the potential of this hybrid framework for extracting richness and generalized temporal features, we propose a new variant of transformer tailored for gait, including temporally shifted tokenization, length-flexible position embedding, and inter-frame encoder. In addition, we introduce an auxiliary task - view label prediction - aiming to disentangle view from ID information. Extensive experimental results on two well-known gait benchmark datasets, CASIA-B and GREW, demonstrate the superior performance of the proposed Gait-CoTr. © 2023 IEEE.

关键词： Convolution

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Distributed Secondary Control of DC Microgrids with Operation Loss Reduction 43

Distributed Secondary Control of DC Microgrids with Operatio...

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43rd Chinese Control Conference, CCC 2024

作者： Qiu, Maoxiang Zhang, Zhicheng Zuo, Zhiqiang Wang, Yijing Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789887581581

In DC microgrids, current sharing and voltage regulation are two main objectives for real-time control, while the operation loss reduction is typically achieved by steady-state optimization. However, the ratio of current sharing is generally determined by the result of the centralized optimization in the classical hierarchical control framework of DC microgrids. Such an issue brings insufficient capability in respond to the changes of photovoltaic power and stored energy in the solar-powered energy systems. To handle this, this work proposes a distributed control scheme for single-bus DC microgrids. Under the proposed controller, the objectives of current sharing, bus voltage regulation and the total operation loss (converter loss and line loss) reduction can be guaranteed simultaneously. Moreover, the constraints on line currents and bus voltage are tackled as well. Finally, simulations verify the performance of the proposed control scheme. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Microgrids

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Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1913-1926页

作者： Eldeeb, Eslam Sifaou, Houssem Simeone, Osvaldo Shehab, Mohammad Alves, Hirley University of Oulu Centre for Wireless Communications Oulu90014 Finland King's College London King's Communications Learning and Information Processing Lab Centre for Intelligent Information Processing Systems Department of Engineering LondonWC2R 2LS United Kingdom King Abdullah University of Science and Technology CEMSE Division Thuwal23955 Saudi Arabia

Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in adopting RL is the need for direct access to the physical environment. This limitation is particularly severe in multi-agent systems, for which conventional multi-agent reinforcement learning (MARL) requires a large number of coordinated online interactions with the environment during training. When only offline data is available, a direct application of online MARL schemes would generally fail due to the epistemic uncertainty entailed by the lack of exploration during training. In this work, we propose an offline MARL scheme that integrates distributional RL and conservative Q-learning to address the environment's inherent aleatoric uncertainty and the epistemic uncertainty arising from the use of offline data. We explore both independent and joint learning strategies. The proposed MARL scheme, referred to as multi-agent conservative quantile regression, addresses general risk-sensitive design criteria and is applied to the trajectory planning problem in drone networks, showcasing its advantages. © 2015 IEEE.

关键词： Adversarial machine learning

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Secure Directional Modulation Technique Based on Spectrum Division Transmission 24

Secure Directional Modulation Technique Based on Spectrum Di...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Chen, Zetian Zheng, Lin Yang, Chao Guilin University of Electronic Technology Key Lab. of Cognitive Radio and Information Processing Ministry of Education Guilin541004 China

ISBN: (纸本)9798350363760

Direction modulation (DM) is a physical layer security (PLS) technology that has attracted extensive research. In this article, we propose a directional modulation scheme based on spectrum division transmission (SDT-DM). The original signal is divided into different sub-spectrum signals by filter bank and a phase shift related to the direction angle is added to each subspectrum signal. By theotical analysis, the received signal in any non-cooperator direction is proved to be subjected to complex inter-symbol and cross-symbol interference, while the combined signal in the cooperator direction is not affected. Simulation results demonstrate that the proposed SDT-DM scheme can achieve narrow secure zone and confuse constellations for noncooperator. © 2024 IEEE.

关键词： Intersymbol interference

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Exploring Query Efficient Data Generation Towards Data-Free Model Stealing in Hard label Setting 39

Exploring Query Efficient Data Generation Towards Data-Free ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pei, Gaozheng Lyu, Shaojie Ma, Ke Yang, Pinci Xu, Qianqian Sun, Yingfei School of Electronic Electrical and Communication Engineering UCAS Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Tencent Corporate China

ISBN: (纸本)157735897X

Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model’s structure, parameters, or training data. The adversary can only access the target model’s predictions for generated samples. Once the substitute model closely approximates the behavior of the target model, attackers can exploit its white-box characteristics for subsequent malicious activities, such as adversarial attacks. Existing methods within cooperative game frameworks often produce samples with high confidence for the prediction of the substitute model, which makes it difficult for the substitute model to replicate the behavior of the target model. This paper presents a new data-free model stealing approach called Query Efficient Data Generation (QEDG). We introduce two distinct loss functions to ensure the generation of sufficient samples that closely and uniformly align with the target model’s decision boundary across multiple classes. Building on the limitation of current methods, which typically yield only one piece of supervised information per query, we propose the query-free sample augmentation that enables the acquisition of additional supervised information without increasing the number of queries. Motivated by theoretical analysis, we adopt the consistency rate metric, which more accurately evaluates the similarity between the substitute and target models. We conducted extensive experiments to verify the effectiveness of our proposed method, which achieved better performance with fewer queries compared to the state-of-the-art methods on the real MLaaS scenario and five datasets. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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SCSGNet: Spatial-Correlated and Shape-Guided Network for Breast Mass Segmentation 48

SCSGNet: Spatial-Correlated and Shape-Guided Network for Bre...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Qingqiu Xu, Jilan Yuan, Runtian Zhang, Yuejie Feng, Rui Fudan University School of Academy for Engineering and Technology Shanghai China Fudan University School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9781728163277

Automatic and accurate breast mass segmentation plays a crucial role in the early diagnosis of breast cancer. However, it has been a challenging task for two main reasons: (1) Breast masses are diverse;and (2) The boundaries of masses are ambiguous. To address these problems, we propose a Spatial-Correlated and Shape-Guided Network (SCSGNet), which combines global context extraction with local boundary refinement. Specifically, the high-level features are aggregated to produce a global map as the initial guidance area, and a Series-Parallel Feature Fusion (SPFF) module is added to capture masses of different shapes and sizes. Besides, we design a Dynamic Long-range Correlation Capture (DLCC) module to capture the spatial correlation of masses at different positions. Finally, we devise a Triplet Attention Guide (TAG) module to iteratively update the feature map and refine the boundary. Experiments on two public datasets demonstrate that our method achieves superior performance over other state-of-the-art methods. © 2023 IEEE.

关键词： Iterative methods

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