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31st International Conference on Multimedia Modeling, MMM 2025

作者： Wang, Yufei Yao, Junfeng Wang, Zefeng Xiamen University Xiamen China Center for Digital Media Computing School of Film Xiamen University Xiamen China Institute of Artificial Intelligence Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9789819620630

Traditional piano learning often requires significant equipment and skill, which can be intimidating for novices. This paper introduces PianoPal, a multimedia piano instruction system based on robotic interaction, designed for novices. The system includes three modules: intelligent performance, piano interaction, and real-time feedback. To test PianoPal’s effectiveness, we conducted two experiments. The first evaluated the fingering planning function using Q-Learning, showing it outperformed alternatives across all metrics. The second experiment involved 24 novice users, with pre- and post-experience questionnaires and interviews indicating that PianoPal improved one- and two-handed performances and was effective in the short term. Overall, PianoPal proves effective in aiding novice piano learning and has been successfully integrated into our robotic piano device, laying a solid foundation for future piano tutoring robot developments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musical instruments

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CPAL: Cross-prompting Adapter with LoRAs for RGB+X Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Ye Wu, Pengfei Wang, Miaohui Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence Nanjing China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications Lancaster United Kingdom

As sensor technology evolves, RGB+X systems combine traditional RGB cameras with another type of auxiliary sensor, which enhances perception capabilities and provides richer information for important tasks such as semantic segmentation. However, acquiring massive RGB+X data is difficult due to the need for specific acquisition equipment. Therefore, traditional RGB+X segmentation methods often perform pretraining on relatively abundant RGB data. However, these methods lack corresponding mechanisms to fully exploit the pretrained model, and the scope of the pretraining RGB dataset remains limited. Recent works have employed prompt learning to tap into the potential of pretrained foundation models, but these methods adopt a unidirectional prompting approach i.e., using X or RGB+X modality to prompt pretrained foundation models in RGB modality, neglecting the potential in non-RGB modalities. In this paper, we are dedicated to developing the potential of pretrained foundation models in both RGB and non-RGB modalities simultaneously, which is non-trivial due to the semantic gap between modalities. Specifically, we present the CPAL (Cross-prompting Adapter with LoRAs), a framework that features a novel bi-directional adapter to simultaneously fully exploit the complementarity and bridging the semantic gap between modalities. Additionally, CPAL introduces low-rank adaption (LoRA) to fine-tune the foundation model of each modal. With the support of these elements, we have successfully unleashed the potential of RGB foundation models in both RGB and non-RGB modalities simultaneously. Our method achieves state-of-the-art (SOTA) performance on five multi-modal benchmarks, including RGB+Depth, RGB+Thermal, RGB+Event, and a multi-modal video object segmentation benchmark, as well as four multi-modal salient object detection benchmarks. The code and results are available at: https://***/abelny56/CPAL. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomposition for Single View RGB-D Videos

PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Jinwen Xie, Weixing Yao, Junfeng Wu, Shaoqi Peng, Youhong Ge, Mengyuan Dong, Xiao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Jiujiang Research Institute of Xiamen University Jiujiang China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798350368741

Surface reconstruction of dynamic scenes from single view videos is a challenging task due to the highly ill-posed and under-constrained nature. Existing single view reconstruction methods suffer from severe quality issues, such as surface distortion and mesh adherison. In this paper, we propose an efficient dynamic representation network, PD-SDF, which consists of a 4D motion field and a 3D geometry field. The explicit disentanglement of motion and geometry based on planar factorization guarantees the mesh consistency across frames. Specifically, to address the mesh adhesion problem, we design a depth-guided sampling strategy to focus on optimizing the SDF field near the object surface. Due to insufficient geometric cues, we design various regularization strategies to constrain smoothness and topological correcness of the scene geometry. Extensive experiments show that our method outperforms existing methods in both appearance and geometry reconstruction. The project page: https://***/. © 2025 IEEE.

关键词： Dynamic Surface Reconstruction Neural SDF Field Single View Reconstruction

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Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow

arXiv

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arXiv 2025年

作者： Zhou, Hanyu Wang, Haonan Liu, Haoyue Duan, Yuxing Chang, Yi Yan, Luxin National Key Lab of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Computing National University of Singapore Singapore

High-dynamic scene optical flow is a challenging task, which suffers spatial blur and temporal discontinuous motion due to large displacement in frame imaging, thus deteriorating the spatiotemporal feature of optical flow. Typically, existing methods mainly introduce event camera to directly fuse the spatiotemporal features between the two modalities. However, this direct fusion is ineffective, since there exists a large gap due to the heterogeneous data representation between frame and event modalities. To address this issue, we explore a common-latent space as an intermediate bridge to mitigate the modality gap. In this work, we propose a novel common spatiotemporal fusion between frame and event modalities for high-dynamic scene optical flow, including visual boundary localization and motion correlation fusion. Specifically, in visual boundary localization, we figure out that frame and event share the similar spatiotemporal gradients, whose similarity distribution is consistent with the extracted boundary distribution. This motivates us to design the common spatiotemporal gradient to constrain the reference boundary localization. In motion correlation fusion, we discover that the frame-based motion possesses spatially dense but temporally discontinuous correlation, while the event-based motion has spatially sparse but temporally continuous correlation. This inspires us to use the reference boundary to guide the complementary motion knowledge fusion between the two modalities. Moreover, common spatiotemporal fusion can not only relieve the cross-modal feature discrepancy, but also make the fusion process interpretable for dense and continuous optical flow. Extensive experiments have been performed to verify the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical flows

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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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VFLMonitor: Defending One-Party Hijacking Attacks in Vertical Federated Learning

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IEEE Transactions on information Forensics and Security 2025年 20卷 4828-4843页

作者： Xu, Xiangrui Zhao, Yiwen Han, Yufei Zhu, Yongsheng Han, Zhen Xu, Guangquan Wang, Bin Ji, Shouling Wang, Wei Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China University of Southern California Thomas Lord Department of Computer Science Los AngelesCA90007 United States INRIA Brittany Rennes35042 France Institute of Computing Technologies China Academy of Railway Sciences Corporation Ltd. Beijing100081 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China College of Intelligence and Computing Tianjin300072 China Network and Data Security Hangzhou310053 China Zhejiang University College of Computer Science and Technology Zhejiang 310058 China Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China

Vertical Federated Learning (VFL) is susceptible to various one-party hijacking attacks, such as Replay and Generation attacks, where a single malicious client can manipulate the model to produce attacker-specified results, thereby compromising its reliability in real-world deployments. In this paper, we first uncover the underlying mechanisms of these attacks and observe that successful attacks induce significant discrepancies in the embedding-label associations across different clients. We establish a theoretical framework demonstrating how these discrepancies can serve as reliable indicators for detecting hijacking attempts. Building upon this insight, we propose VFLMonitor, a robust defense mechanism that leverages these embedding-label discrepancies to detect and mitigate hijacking attacks. Specifically, VFLMonitor identifies suspicious queries by analyzing differences in label estimations from multiple clients and applies a majority voting rule to correct or filter out these malicious queries. Moreover, VFLMonitor introduces a novel regularization strategy during training to reduce intra-class variance in embeddings, thereby enhancing their discriminative power and improving defense effectiveness. Extensive experiments were conducted on 5 real-world datasets against 2 different attack types under 3 attack scenarios. The results demonstrate that VFLMonitor can effectively identify and exclude potential hijacked requests in all types of one-party hijacking attacks, while maintaining a meager false positive rate for legitimate queries. © 2005-2012 IEEE.

关键词： Training Servers Electronic mail Artificial intelligence Reliability theory Predictive models Federated learning Data models Computational modeling Collaboration

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FDAC: Federated Domain Adaptation via Dual Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yi, Chang'an Chen, Haotian Zhang, Yifan Xu, Yonghui Zhou, Yan Cui, Lizhen Yu, Han Foshan University Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology School of Electronic and Information Engineering Foshan China Jinan China School of Computing Singapore Singapore College of Computing and Data Science Singapore Singapore

Federated domain adaptation (FDA) aims to transfer knowledge collaboratively from multiple source domains to related but different unlabelled target domains. The data of each domain are locally maintained, and various domain gaps exist among them, resulting in extreme challenges in simultaneously mitigating diverse distribution shifts and preserving discriminative knowledge without accessing the source data. Many existing works have failed to fully explore different source models to measure domain shifts and leverage semantic knowledge, resulting in skewed alignment and partial preservation of discriminative information. In this paper, we propose a novel approach named FDAC to address Federated Domain Adaptation by thoroughly investigating source models via dual Contrastive mechanisms. FDAC contrastively increases the data diversity to align features across domains in a fine-grained manner by manipulating the latent deep architecture and compensating for knowledge from each source domain;simultaneously, it contrastively utilizes the comprehensive semantic knowledge of different source domains to guide the adaptation process. Extensive experiments on several real datasets demonstrate that FDAC outperforms all comparative methods under most conditions. Furthermore, FDAC only needs approximately half of the communication rounds compared with the state-of-the-art methods, indicating that FDAC can significantly improve communication efficiency, which is another key factor in the federated setting. The source code is publicly available at https://***/ycarobot/FDAC. © 1991-2012 IEEE.

关键词： Contrastive learning Domain adaptation Federated learning Vision transformer

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Multi-scale compression artifact attention-driven compressed video quality enhancement

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Digital Signal processing: A Review Journal 2025年 163卷

作者： Lan, Chengdong Lin, Wei Liang, Haolin Kang, Yuanxun College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350116 China Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fujian Fuzhou350116 China Yealink Fujian Xiamen61009 China

Compressed video quality enhancement is crucial for mitigating artifacts introduced by video coding. Video compression often results in unevenly distributed artifacts across different regions of video frames, leading to significant quality variations. Existing algorithms treat all regions uniformly, ignoring these localized differences, limiting their ability to extract high-quality information from reference frames and accurately reconstruct residuals. Additionally, larger temporal gaps between reference and target frames can cause alignment errors, which propagate during fusion and degrade performance. To address these challenges, we propose a Multi-scale Compression Artifact Attention (MSCAA) module that captures artifact distribution, guiding the model to focus on distorted regions. We also introduce a Progressive Fusion Stage that sequentially fuses reference frames based on temporal proximity, reducing error propagation and enhancing temporal coherence. The experimental results show that the proposed method improves average ΔPSNR by 5.15% and ΔSSIM by 3.35% compared to the state-of-the-art method, demonstrating its superior performance in quality enhancement. © 2025 Elsevier Inc.

关键词： Compression artifacts Deep learning Video quality enhancement

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An enhanced sparse subset selection model based on adaptive projection distance

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Evolutionary Intelligence 2025年第3期18卷 1-15页

作者： Feng, Feng Gao, Erwei Zhi, Xiaobin Zhao, Yichuan School of Science and Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an 710121 China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an 710121 China School of Science and Research Center for Intelligent Systems and Data Science Xi’an University of Posts and Telecommunications Xi’an 710121 China Department of Mathematics and Statistics Georgia State University Atlanta 30303 GA United States

Subset selection focuses on identifying representative samples from a large dataset to produce a data subset that can represent the main features of the original data and also reduce the data size in an effective way. Sparse representation theory puts emphasis on enhancing the data sparsity, while subset selection aims to reduce the data size. Sparse subset selection is an important branch of subset selection that incorporates sparse representation theory into the exploration of subset selection. Sparse modeling representative selection model has demonstrated outstanding performance in data classification and video summarization. Nonetheless, it suffers from the issue of poor typicality of the selected representatives. Although some recent works have addressed this problem to some extent by introducing the concept of clustering coding terms, most of the clustering coding terms constructed by these existing methods are implicit, and the model structure is complicated, inefficient, and difficult to be understood. To overcome these difficulties, we propose an adaptive projection distance-based sparse subset selection model. This model constructs a clustering coding item by adaptively calculating the projection distance between data. It can not only eliminate redundant and noisy features in the original data but also ensure that the selected representatives are more typical. To solve the proposed model, an efficient algorithm is developed by virtue of the alternating direction method of multipliers. Extensive experiments with comparative analysis demonstrate that the proposed model outperforms several state-of-the-art models in various tasks including subset selection, cluster analysis, classification, and face image representative selection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Adaptive projection distance Classification Cluster analysis Sparse representation Subset selection

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NetPrompt: Neural Network Prompting Enhances Event Extraction in Large Language Models

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IEEE Transactions on Big Data 2025年

作者： Mu, Lin Cheng, Yide Shen, Jun Zhang, Yiwen Zhong, Hong School of Computer Science and Technology Anhui University Anhui Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education the Anhui Engineering Laboratory of IoT Security Technologies the School of Computer Science and Technology the Institute of Physical Science and Information Technology Anhui University Hefei230039 China

Event Extraction involves extracting event-related information such as event types and event arguments from context, which has long been tackled through well-designed neural networks or fine-tuned pre-trained language models. These approaches require substantial annotated data for tuning parameters and are resource-intensive. Recently, Prompting strategies with frozen parameters, such as Chain-of-Thought and Self-Consistency, have delivered success in NLP using LLMs by generating intermediate thought steps. However, they suffer from the challenge of error propagation and lack of interaction between different thoughts. In this paper, we propose Neural Network-based Prompting (NetPrompt), a novel network-structured prompting strategy for event extraction. The core idea behind NetPrompt is to imitate the excellent information integration capabilities of neural network structures. Specifically, we first decompose the event extraction problem into diverse intermediate subtasks, and each subtask is represented as a node in different layers of the network, the output of the nodes in the preceding layer is fed into the subsequent layer. Secondly, we propose pruning strategies to adapt the reasoning overhead to different problems. Finally, we have conducted extensive experiments on two widely used event extraction benchmarks to evaluate NetPrompt. The results demonstrated that NetPrompt significantly improved the event extraction performance compared to previous methods. ©2015 IEEE.

关键词： Neural network models

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