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

作者： Zhuang, Ning Wei, Xiao Li, Junlei Wang, Xiaobao Wang, Chenyang Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

Intent detection (ID) is essential in spoken language understanding, especially in multi-label settings where intent labels are interdependent and diverse. Existing methods like SE-MLP and QA-FT struggle in few-shot settings, due to limited data availability and efficiency concerns. To address this, we introduce a Prompt Learning framework with large language Model Augmentation (PLMA) for few-shot multi-label ID. In this study, we make three contributions. First, PLMA integrates large language models (LLMs) with small language models (SLMs), using LLMs to enhance SLMs, with prompt learning as the core framework. Second, it leverages LLMs to improve the model's understanding of both queries and labels by intent spans extraction and answer space expansion. Third, PLMA refines the QA-FT. A novel QA format template is designed to allow single-step training and inference for each input, improving efficiency. Experiments on NLU++ show that PLMA significantly outperforms other baselines in both in-domain and cross-domain settings, demonstrating its effectiveness in combining the strengths of both small and large models. © 2025 IEEE.

关键词： large language model multi-label intent detection prompt learning spoken language understanding

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MaintaAvatar: A Maintainable Avatar Based on Neural Radiance Fields by Continual Learning

arXiv

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arXiv 2025年

作者： Gu, Shengbo Qiu, Yu-Kun Tang, Yu-Ming Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The generation of a virtual digital avatar is a crucial research topic in the field of computer vision. Many existing works utilize Neural Radiance Fields (NeRF) to address this issue and have achieved impressive results. However, previous works assume the images of the training person are available and fixed while the appearances and poses of a subject could constantly change and increase in real-world scenarios. How to update the human avatar but also maintain the ability to render the old appearance of the person is a practical challenge. One trivial solution is to combine the existing virtual avatar models based on NeRF with continual learning methods. However, there are some critical issues in this approach: learning new appearances and poses can cause the model to forget past information, which in turn leads to a degradation in the rendering quality of past appearances, especially color bleeding issues, and incorrect human body poses. In this work, we propose a maintainable avatar (MaintaAvatar) based on neural radiance fields by continual learning, which resolves the issues by utilizing a Global-Local Joint Storage Module and a Pose Distillation Module. Overall, our model requires only limited data collection to quickly fine-tune the model while avoiding catastrophic forgetting, thus achieving a maintainable virtual avatar. The experimental results validate the effectiveness of our MaintaAvatar model. © 2025, CC BY.

关键词： Federated learning

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iManip: Skill-Incremental Learning for Robotic Manipulation

arXiv

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arXiv 2025年

作者： Zheng, Zexin Cai, Jia-Feng Wu, Xiao-Ming Wei, Yi-Lin Tang, Yu-Ming Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Pengcheng Lab China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task, skill-incremental learning, in robotic manipulation, which is to endow the robots with the ability to learn new manipulation skills based on the previous learned knowledge without re-training. First, we build a skill-incremental environment based on the RLBench benchmark, and explore how traditional incremental methods perform in this setting. We find that they suffer from severe catastrophic forgetting due to the previous methods on classification overlooking the characteristics of temporality and action complexity in robotic manipulation tasks. Towards this end, we propose an incremental Manipulation framework, termed iManip, to mitigate the above issues. We firstly design a temporal replay strategy to maintain the integrity of old skills when learning new skill. Moreover, we propose the extendable PerceiverIO, consisting of an action prompt with extendable weight to adapt to new action primitives in new skill. Extensive experiments show that our framework performs well in Skill-Incremental Learning. Codes of the skill-incremental environment with our framework will be open-source. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot learning

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Class Semantic Prompts Enhanced Prototypical Fusion Method for Few-shot Named Entity Recognition

Class Semantic Prompts Enhanced Prototypical Fusion Method f...

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

作者： Yu, Mei Tao, Yuang Zhao, Mankun Xu, Tianyi Meng, Zechen Zhang, Wenbin Yu, Jian School of Future Technology Tianjin University Tianjin300350 China College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Information and Network Center Tianjin University Tianjin300350 China

ISBN: (纸本)9798350368741

Few-shot named entity recognition is to identify named entities in scenarios where labeled data is scarce. Existing prototype building methods ignore the use of class semantic and it is difficult to obtain accurate prototype representations only by relying on few support samples. In this paper, we propose a class semantic prompts enhanced prototypical fusion method (CSFP). Specifically, we design a class-semantic prototype that adapts to current task through prompts. In order to add intra-class similarity to the existing prototype and obtain a more accurate and stable prototype representation, we consider the samples distribution and fuse class-semantic prototype with existing prototype through a weighted strategy. Experimental results on two few-shot NER benchmarks show that our method outperforms previous SOTA methods. The analysis further verifies the effectiveness of our method. © 2025 IEEE.

关键词： class semantic few-shot NER prompt learning prototype

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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OCLNet: Obfuscation feature Contrastive Learning Network for Weakly Supervised Semantic Segmentation on Ultrasound Images

OCLNet: Obfuscation feature Contrastive Learning Network for...

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

作者： Jie Gao Xianzhi Zhang Zijian Zhang Xuewei Li Mei Yu Ruiguo Yu Zhiqiang Liu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application of fully supervised semantic segmentation models. To address this, weakly supervised learning-based semantic segmentation models have emerged as a promising solution. These models can accurately segment lesion regions using only weak annotations, such as image-level or frame-level labels, significantly reducing the annotation burden. This approach has gained substantial attention in current *** various medical imaging modalities, ultrasound imaging stands out as a primary diagnostic tool due to its rapid imaging speed, ease of use, and accessibility. This paper focuses on the study of thyroid ultrasound imaging, aiming to achieve accurate classification of nodule regions. The goal is to provide clinicians with more precise diagnostic information, improving decision-making in thyroid disease diagnosis.

关键词： Ultrasonic imaging Accuracy Annotations Semantic segmentation Scalability Contrastive learning Lesions Speech processing Medical diagnostic imaging Thyroid

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A Novel Network for Short-Term Wind Speed Prediction: Mitigating Distribution Shift and Feature Loss

A Novel Network for Short-Term Wind Speed Prediction: Mitiga...

引用

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

作者： Mei Yu Shengkang Dong Xuewei Li Zewen Shang Yingzhou Sun Zhiqiang Liu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate wind speed forecasting is essential for mitigating the challenges of wind power grid integration. However, existing wind speed prediction models overlook the distributional shift problem within wind speed series, and this time-varying distribution can significantly impact wind prediction accuracy. In this paper, we propose the Distribution Shift and Feature Decoupling Network (DSFD-Net), which addresses the issue of distributional shifts occurring both within the input series and between the input and predicted series through a distribution matching model and distribution mapping module, respectively. Additionally, we introduce a feature decoupling module to mitigate the feature loss encountered in our work. We conduct extensive experiments on two datasets, and comprehensive experimental results demonstrate that DSFD-Net achieves at least a 4.1% reduction in error metrics compared to other wind speed forecasting models, indicating superior performance.

关键词： Measurement Accuracy Wind speed Predictive models Signal processing Acoustics Forecasting Wind forecasting Speech processing Wind energy integration

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Audio-Visual Collaborative Learning for Weakly Supervised Video Anomaly Detection

引用

IEEE Transactions on Multimedia 2025年

作者： Meng, Jingke Tian, Huilin Lin, Ge Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Shenzhen518005 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

Weakly supervised anomaly detection is to identify the time window when an anomaly event happened based on the video-level label indicating whether the video contains anomaly event or not. Recent efforts have focused on leveraging multi-modal data, specifically combining visual and audio information, to enhance detection accuracy. While some studies have explored intra-video separation techniques, the primary emphasis remains on distinguishing potentially anomalous events scoring highest from those scoring lowest as normal events. Nevertheless, challenges persist in delineating boundaries between normal and abnormal events, particularly when visual differences are subtle. Our proposed framework, called Audio-Visual Collaborative Learning (AVCL), addresses the challenge of ambiguity in weakly supervised anomaly detection. Our core idea centers around utilizing both audio track variations and the perceptual robustness of visual information to detect and differentiate challenging cases, which composed of two essential modules: the Audio-Visual collaborative Hard cases Separation (AVHS) module and the Multi-modal Mutual Learning (MML) module. The AVHS module aims to address the challenge of discerning visually ambiguous clips in anomaly videos, differentiating between normal and abnormal events. To further improve detection accuracy, we introduce the Multi-modal Mutual Learning (MML) module, and this module enables a process of mutual learning to facilitate the exchange of knowledge and expertise between the single-modal branch and the multi-modal branch. We demonstrate that the proposed approach achieves state-of-the-art detection performance on benchmarks of XD-Violence and CCTV-Fightssub datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Synthesis of interfacial electric field-enhanced CdS/CdxZn1-xS/ZnO ternary heterojunction by lye dissolution etching mechanism for photocatalytic H2 production and CO2 reduction

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材料科学技术（英文版） 2025年第1期204卷 152-165页

作者： Qi Li Shengchao Yang Yufan Huang Yuwei Liang Chunling Hu Min Wang Zhiyong Liu Yanlong Tai Jichang Liu Yongsheng Li School of Chemistry and Chemical Engineering Shihezi University/Key Laboratory of Green Process for Chemical Engineering/Key Laboratory for Chemical Materials of Xinjiang Uygur Autonomous Region/Engineering Center for Chemical Materials of Xinjiang BingtuanShihezi UniversityShihezi 832003China Shanghai Institute of Ceramics Chinese Academy of SciencesShanghai 200050China School of Chemistry and Chemical Engineering Shihezi University/Key Laboratory of Green Process for Chemical Engineering/Key Laboratory for Chemical Materials of Xinjiang Uygur Autonomous Region/Engineering Center for Chemical Materials of Xinjiang BingtuanShihezi UniversityShihezi 832003China Key Laboratory of Human-Machine Intelligence-Synergy Systems of Chinese Academy of Sciences(CAS) Shenzhen Institutes of Advanced TechnologyShenzhen 518055China School of Chemistry and Chemical Engineering Shihezi University/Key Laboratory of Green Process for Chemical Engineering/Key Laboratory for Chemical Materials of Xinjiang Uygur Autonomous Region/Engineering Center for Chemical Materials of Xinjiang BingtuanShihezi UniversityShihezi 832003China Lab of Low-Dimensional Materials Chemistry Key Laboratory for Ultrafine Materials of Ministry of Education School of Materials Science and Engineering East China University of Science and Technology ShanghaiShanghai 200237China

The difficulty in fabricating a multifaceted composite heterojunction system based on CdxZn1-xS limits the enhancement of photocatalytic *** the present scrutiny,novel ZnO/CdxZn1-xS/CdS com-posite heterojunctions are successfully prepared by the alkaline dissolution etching *** internal electric field at the interface of Ⅰ-type and Z-scheme heterojunction improved the effective charge *** ZC 8 sample exhibits excellent photocatalytic performance and the H2 production efficiency is 15.67 mmol g-1 h-1 with good stability up to 82.9％in 24-hour *** performance of CH4 and CO capacity in the CO2RR process is 3.47 μmol g-1 h-1 and 23.5 μmol g-1 h-1,*** photogener-ated accelerated charge transport is then examined in detail by in situ X-ray photoelectron spectroscopy(ISXPS)and density functional theory(DFT)*** work presents a new idea for the synthe-sis of CdxZni-xS solid-solution-based materials and provides a solid reference for the detailed mechanism regarding the electric field at the heterojunction interface.

关键词： Photocatalysis Interface electric field Composite heterostructure Photocatalytic mechanism CdxZn1-xS solid-solution

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TightLLM: Maximizing Throughput for LLM Inference via Adaptive Offloading Policy

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IEEE Transactions on Computers 2025年

作者： Hu, Yitao Liu, Xiulong Yang, Guotao Li, Linxuan Zeng, Kai Zhao, Zhixin Chen, Sheng Zhao, Laiping Li, Wenxin Li, Keqiu Tianjin University Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Tianjin University Department of Intelligence and Computing Tianjin300350 China

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, largely due to their substantial model size. However, this also results in significant GPU memory demands during inference. To address these challenges on hardware with limited GPU memory, existing approaches employ offloading techniques that offload unused tensors to CPU memory, thereby reducing GPU memory usage. Since offloading involves data transfer between GPU and CPU, it introduces transfer overhead. To mitigate this, prior works typically overlap data transfer with GPU computation using a fixed pipelining strategy applied uniformly across all inference iterations, referred to as static offloading. However, static offloading policies fail to maximize inference throughput because they cannot adapt to the dynamically changing transfer overhead during the inference process, leading to increasing GPU idleness and reduced inference *** propose that offloading policies should be adaptive to the varying transfer overhead across inference iterations to maximize inference throughput. To this end, we design and implement an adaptive offloading-based inference system called TightLLM with two key innovations. First, its key-value (KV) distributor employs a trade-compute-for-transfer strategy to address growing transfer overhead by dynamically recomputing portions of the KV cache, effectively overlapping data transfer with computation and minimizing GPU idleness. Second, TightLLM’s weight loader slices model weights and distributes the loading process across multiple batches, amortizing the excessive weight loading overhead and significantly improving throughput. Evaluation across various combinations of GPU hardware and LLM models shows that TightLLM achieves 1.3 to 23 times higher throughput during the decoding phase and 1.2 to 22 times higher throughput in the prefill phase compared to state-of-the-art offloading systems. Due to the higher throughput in prefill

关键词： Decoding

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