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31st International Conference on Multimedia Modeling, MMM 2025

作者： Li, Xiuhong Zhu, Xinyue Li, Boyuan Li, Songlin Wang, Luyao Jia, Zhenhong School of Computer Science and Technology Xinjiang University Urumgi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China College of Mathematics and System Science Xinjiang University Urumqi China

ISBN: (纸本)9789819620609

Infrared small target detection has received widespread application and attention in both civilian and military fields. However, due to the very small size and lack of unique features of these targets, existing methods often suffer from inaccurate edge localization, and the targets are easily overwhelmed by complex backgrounds. To effectively address these issues, we designed a Feature Refinement and Context Enhancement Network (FCNet). The network consists of a multi-branch feature extraction module (MFEM), which integrates ordinary convolution and center difference convolution for diversified feature extraction, and further refines features through attention mechanism to enhance feature expression ability. In addition, in order to better capture the contextual information of the target, we have introduced the Context Enhancement Module (CEM). The CEM improves the robustness and detection accuracy of the network in complex backgrounds by preserving and enhancing important information in the image. Especially when processing infrared images with complex backgrounds and low signal-to-noise ratios, CEM can effectively highlight the target area and reduce false positives and false negatives. The experimental results on the SIRST dataset show that our FCNet performs well on multiple evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrared imaging

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Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segmentation Network Based on Local Attention and Mamba 27th

Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segment...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Feifei Shi, Fei Ren, Dayong Jia, Zhenhong Wang, Jianyi School of Computer Science and Technology Xinjiang University Urumqi Xinjiang830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang830046 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9783031781032

Brain tissue segmentation is critical for diagnosing and treating brain diseases. While Mamba-based models excel in the medical field, they face performance bottlenecks with high-resolution MRI images, often losing local feature information in complex texture structures. To address these challenges and enable deployment in resource-limited settings, we propose Dual-MambaNet, a lightweight segmentation model based on Mamba. In Dual-MambaNet, we introduce the Outlook attention module to capture local complex textures and structures in brain MRI images. Subsequently, we combined it with the Mamba block to construct a feature extractor (FE) encoder layer to couple local and global features. Additionally, we integrate dual decoder branches and a multi-level pixel contrastive loss function(MPCL) to better integrate local and global features. This method optimizes global feature representation by refining local complex textures and structural details, effectively capturing multi-level features in MRI images. Experimental results on public brain MRI datasets OASIS1 and MRBrainS13 demonstrate that Dual-MambaNet achieves high segmentation accuracy with minimal parameters and computational complexity, making it suitable for deployment in resource-limited medical environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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E-RNS : Enhancing Negative Sample Quality from Gradient Perspective for Graph Recommendation

E-RNS : Enhancing Negative Sample Quality from Gradient Pers...

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

作者： Qiangsheng Feng Jiwei Qin Jie Ma School of Computer Science and Technology Xinjiang University Key Laboratory of Signal Detection and Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bayesian Personalized Ranking (BPR) is a widely used optimization function in GNN-based recommender systems, and negative samples are usually obtained through the Random Negative Sampling (RNS) method during BPR training. However, from the gradient perspective, RNS tends to select low-quality samples with minimal information, resulting in small gradients. These small gradients contribute little to BPR optimization, limiting the model’s ability to effectively distinguish between positive and negative samples. To alleviate this issue, we propose a general negative sample information enhancement method: Enhancing Random Negative Sampling (E-RNS), which constructs hard negative samples by enhancing the information in randomly selected negative samples. Specifically, in the Noise Injection step, it generates initial noise and injects a certain amount of noise in the same direction into the vector dimensions of positive samples to create enriched information. Then, in the Information Fusion step, this enriched information is mixed with the negative samples to synthesize new hard negative samples. Extensive experiments demonstrate that applying E-RNS to GNN-based recommender models significantly improves performance.

关键词： Training Limiting Noise Information filters Vectors Acoustics Bayes methods Speech processing Optimization Recommender systems

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Domain-adaptive transfer network for visual–textual cross-domain sentiment classification

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The Journal of Supercomputing 2025年第7期81卷 1-30页

作者： Wang, Yuan Tohti, Turdi Han, Dongfang Zuo, Zicheng Liang, Yi Liao, Yuanyuan Yang, Qingwen Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

Cross-domain sentiment analysis aims to address the problem of insufficient labeled data by transferring invariant knowledge across domains. Existing studies focus on unimodal domain transfer, but modal differences hinder the transfer of domain information and limit the acquisition of domain-invariant knowledge in multimodal data. Therefore, we propose a domain-adaptive transfer network (DATN) for multimodal cross-domain sentiment analysis. The joint representation is acquired through a bidirectional visual–textual interactive fusion network, and adversarial discriminative domain adaptation is employed to learn the marginal domain shared knowledge in the joint representation. The performance of multimodal domain adaptive modules aligns with conditional distributions. Extensive experiments on public and self-constructed datasets demonstrate the effectiveness of the model and show that self-constructed datasets have the potential to serve as a new benchmark. Compared with the best-performing method, the model’s accuracy on public datasets increased by 3.6% and 8.2%, respectively, and on self-built datasets by 9.2% and 3.3%, respectively.

关键词： Labeled data

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Improving Speaker Verification Back-End with Graph Neural Networks

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Chen, Jinfeng Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

Currently, research on speaker verification tasks is primarily concentrated on enhancing deep speaker models to extract high-quality speaker embeddings. Nevertheless, this speaker embeddings can be regarded as potential graph structures. Therefore, this paper introduces a graph neural network-based speaker verification back-end approach, treating the speaker embeddings derived from the front-end as graph structures and leveraging graph neural networks to uncover the relationships among embeddings, thereby achieving superior-quality speaker embeddings. In addition, we propose a group updating method to solve the problem of memory overflow when the number of nodes is excessive. Extensive experiments and ablation studies were carried out on the VoxCeleb dataset, and the experimental outcomes validate the effectiveness of our proposed graph neural network speaker back-end in significantly boosting the performance of speaker verification systems. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Graph embeddings

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Anomalous Sound detection Using Time-Frequency Feature and Mixbatch

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Journal of Shanghai Jiaotong University (Science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Prompt Fusion and Aspect-Oriented Filtration for Aspect-Based Multimodal Sentiment Analysis

Prompt Fusion and Aspect-Oriented Filtration for Aspect-Base...

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

作者： Sun, Jiachang Li, Xiuhong Zhu, Fuxian School of Computer Science and Technology XinJiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830046 China School of Economics and Management XinJiang University Urumqi830046 China

ISBN: (纸本)9798350368741

Currently, aspect-based multimodal sentiment analysis (ABMSA) remains a highly hot research field, aiming to leverage various modalities such as images and text to determine the sentiment orientation of viewpoint entities. Although deep learning methods have made significant progress in this field, some challenges still exist: Image information unrelated to the viewpoint entity can introduce noise, insufficient interaction during modal fusion. To solve these problems, we propose a novel ABMSA model with prompt fusion and noise filtration. Specifically, we introduced an Image Noise Filtering (INF) module that can filter the extracted image features to reduce the noise introduced by the irrelevant between viewpoint entity and the image. Additionally, we implemented Multimodal Prompt Fusion (MPF) module to enhance the interaction between modalities, addressing the issue of minimal effective features related to viewpoint entity during the fusion stage of text and image features. Experimental results show that the model outperforms the baseline model on two public datasets, Twitter-2015 and Twitter-2017, demonstrating that our proposed approach effectively enhances the accuracy of aspect-based multimodal sentiment analysis tasks. © 2025 IEEE.

关键词： Aspect-Based Sentiment Analysis Multimodal Sentiment Analysis Noise Filtration Prompt Fusion

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Short-term wind power prediction with a new PCC-GWO-VMD and BiGRU hybrid model enhanced by attention mechanism

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Journal of Renewable and Sustainable Energy 2025年第3期17卷

作者： Zhang, Xiaoyu Qin, Xizhong Qin, Jiwei Wang, Ben Yuan, Yubo School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830046 China

The integration of wind power into the grid significantly depends on the accuracy and reliability of wind power prediction. However, the task of wind power prediction faces significant challenges due to the stochastic nature of wind speed. This study proposes a novel deep hybrid short-term wind power prediction model: PCC-GWO-VMD-BiGRU-Attention. The model integrates Pearson correlation coefficient (PCC), variational mode decomposition (VMD), gray wolf optimization (GWO), and attention mechanism optimized bidirectional gated recurrent unit (BiGRU-Attention) to enhance prediction accuracy and robustness, while quantifying prediction uncertainty through deep ensemble methods. First, PCC is used to identify the key factors affecting wind power, thereby improving the model's computational performance. Second, GWO can dynamically adjust the key parameters [K, α ] in VMD for optimal decomposition of the input time series, thereby improving prediction accuracy. Finally, the BiGRU-Attention model is utilized to extract global temporal features in historical sequences, while the attention mechanism focuses on key information in the sequences to further enhance prediction performance. Experimental results show that, compared to other deep learning models, this model achieves the highest accuracy in short-term wind power prediction with an root mean square error of 0.2677 Megawatt (MW), mean absolute error of 0.1509 Megawatt (MW), mean square error of 0.0717 Megawatt (MW), and coefficient of determination (R2) of 0.9605. This method has significant practical value and contributes to ensuring safe operation of wind farms. © 2025 Author(s).

关键词： Deep learning

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RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model 31

RGR-KBQA: Generating Logical Forms for Question Answering Us...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Feng, Tengfei He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798891761964

In the field of natural language processing, Knowledge Base Question Answering (KBQA) is a challenging task that involves accurately retrieving answers from structured knowledge. Existing methods often face issues when generating query statements using LLMs, as the knowledge introduced may be imprecise and the models themselves may exhibit hallucination problems, leading to low accuracy, particularly when dealing with complex questions. To address these challenges, we introduce a novel semantic parsing approach called RGR-KBQA, which adopts a Retrieve-Generate-Retrieve framework. The first retrieval step introduces factual knowledge from a knowledge graph to enhance the semantic understanding capabilities of LLMs, thereby improving generation accuracy of logical form. The second step uses a fine-tuned model to generate the logical form, and the final step involves unsupervised relation and entity retrieval to further enhance generation accuracy. These two retrieval steps help alleviate the hallucination problems inherent in LLMs. Experimental results show that RGR-KBQA demonstrate promising performance on CWQ and WebQSP datasets. © 2025 Association for Computational Linguistics.

关键词： Question answering

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