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检索条件"机构=Key Laboratory for Digital Imaging Processing and Intelligent Computing"

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PRPFD: Polynomial Resampling Particle Filter Based Deconvolution Approach for Resting-State BOLD-fMRI Data Analysis 24

PRPFD: Polynomial Resampling Particle Filter Based Deconvolu...

引用

5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Luo, Yunying Zeng, Weiming Key Laboratory for Digital Imaging Processing and Intelligent Computing School of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400717826

Functional Magnetic Resonance imaging (fMRI) stands out in brain science research due to its non-invasive, non-intrusive, radiation-free, high spatial resolution, and precise localization advantages. However, the BOLD-fMRI signals it generates exhibit a several-second delay compared to neural activity, making it unable to reflect brain neural activity directly. This discrepancy may lead to differences from the actual situation. Therefore, this study aims to enhance the deconvolution of fMRI by refining existing methods to reconstruct the brain’s neural signals more faithfully. Building upon the foundation of particle filtering deconvolution, this paper introduced a novel approach to deconvolving resting-state fMRI data: the polynomial resampling particle filtering deconvolution (PRPFD). They adapted the particle distribution better to match the actual state through weight computation and resampling. We performed a simulation-based experiment to validate the feasibility of our approach. Besides, we validated the performance of our method using group data, focusing on both effective connectivity and functional connectivity, providing strong evidence for the usability and stability of our approach, and compared our method with other deconvolution techniques, demonstrating improvements in accuracy and computational efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Polynomials

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Representation Purification for End-to-End Speech Translation 31

Representation Purification for End-to-End Speech Translatio...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Chengwei Zhou, Yue Zhao, Rui Chen, Yidong Shi, Xiaodong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China

ISBN: (纸本)9798891761964

Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language. Previous research primarily focused on enhancing speech translation by facilitating knowledge transfer from machine translation, exploring various methods to bridge the gap between speech and text modalities. Despite substantial progress made, factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. In this paper, we conceptualize speech representation as a combination of content-agnostic and content-relevant factors. We examine the impact of content-agnostic factors on translation performance through preliminary experiments and observe a significant performance deterioration when content-agnostic perturbations are introduced to speech signals. To address this issue, we propose a Speech Representation Purification with Supervision Enhancement (SRPSE) framework, which excludes the content-agnostic components within speech representations to mitigate their negative impact on ST. Experiments on MuST-C and CoVoST-2 datasets demonstrate that SRPSE significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a transcript-free setting. © 2025 Association for Computational Linguistics.

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LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

引用

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

作者： Wu, Shaoqi Xie, Weixing Peng, Youhong Li, Jinwen Yao, Jiawei Yao, Junfeng 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 Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Taiwan

ISBN: (纸本)9798350368741

With the popularity of short video platforms, the number of single-view videos has increased significantly. Existing NeRF-based methods can reconstruct dynamic scenes in a single-view setting, but slow rendering speed and low rendering quality limit their practical applications. To address these challenges, we propose a fast single-view scenes reconstruction framework based on 3D Gaussian Splatting. Our method uses point clouds obtained with depth priors as the Gaussian initialization and introduces learnable parametric functions to model the time-dependent deformation of Gaussians. The explicit deformation modeling for Gaussians significantly reduces training and rendering time. Furthermore, to improve the rendering quality of challenging areas, we adopt an adaptive sampling strategy to densify Gaussians. For occlusion problems from single-view videos, we design a smooth loss function to restore the color of the occluded areas. Experimental results demonstrate that our method significantly reduces training time, enhances rendering quality, and accelerates rendering speed. Project page: https://***/LPGaussians. © 2025 IEEE.

关键词： 3D Gaussian Splatting Dynamic Scene Reconstruction Single-view Reconstruction

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

引用

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

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing 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)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language processing

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AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification 31

AGCL: Aspect Graph Construction and Learning for Aspect-leve...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jian, Zhongquan Wu, Daihang Wang, Shaopan Wang, Yancheng Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China College of Management Mahidol University Thailand School of Computing and Data Science Xiamen University Malaysia Malaysia Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9798891761964

Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed at furnishing the model with finely tuned aspect information to bolster its task-understanding ability. AGCL's pivotal innovations reside in Aspect Graph Construction (AGC) and Aspect Graph Learning (AGL), where AGC harnesses intrinsic aspect connections to construct the domain aspect graph, and then AGL iteratively updates the introduced aspect graph to enhance its domain expertise, making it more suitable for the ALSC task. Hence, this domain aspect graph can serve as a bridge connecting unseen aspects with seen aspects, thereby enhancing the model's generalization capability. Experiment results on three widely used datasets demonstrate the significance of aspect information for ALSC and highlight AGL's superiority in aspect learning, surpassing state-of-the-art baselines greatly. Code is available at https://***/jian-projects/agcl. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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PianoPal: A Robotic Multimedia System for Interactive Piano Instruction Based on Q-Learning and Real-Time Feedback 31st

PianoPal: A Robotic Multimedia System for Interactive Piano...

引用

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

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

引用

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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PaintingScapeVR: Exploring Gamified Interaction in Virtual Galleries 1st

PaintingScapeVR: Exploring Gamified Interaction in Virtual ...

引用

1st International Conference on Extended Reality, ICXR 2024

作者： Cai, Dongning Ren, Le Chang, Yaqi Li, Xiaodan Lin, Yuxin Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism New Taipei City Taiwan School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9789819636785

In reality, galleries usually display paintings in a protective manner, partly to delay the damage to the original artwork and ensure the long-term preservation of the artwork. On the other hand, it also hinders visitors’ deep appreciation and learning of the artwork. In the face of this challenge, our research aims to provide users with immersive viewing and entertainment, as well as interactive gaming experiences with artwork, through the use of virtual reality technology. So the paper proposes PaintingScapeVR, which is based on a virtual gallery and reproduces the three-dimensional scenes corresponding to different paintings. Visitors can freely explore in this virtual environment and gain a deeper understanding of the meaning and artistic value of artworks through interaction with them. Our results indicate that the reproduction of 3D scenes and gamified interactive design can not only significantly enhance visitors’ appreciation of painting art, but also leave a deeper learning impression. This immersive experience stimulates visitors’ desire to appreciate and explore, prompting them to actively delve into the background and techniques of art works, thereby enhancing their artistic appreciation abilities. At the same time, this work also demonstrates its potential in enhancing visitors’ understanding of painting works, allowing more people to explore the joy of painting and art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interactive gamification Three-dimensional displays Virtual reality

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

引用

IEEE International Symposium on Biomedical imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shaoqi Wu Weixing Xie Youhong Peng Jinwen Li Jiawei Yao Junfeng Yao 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 Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Point cloud compression Deformable models Adaptation models Three-dimensional displays Deformation Streaming media Signal processing Rendering (computer graphics) Speech processing

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