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Causal-TSF: A Causal Intervention Approach to Mitigate Confounding Bias in Time Series Forecasting

IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3205-3219页

作者： Gong, Qinkang Pan, Yan Lai, Hanjiang Qiu, Rongbang Yin, Jian Sun Yat-sen University School of Artificial Intelligence Zhuhai519082 China Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China Sun Yat-sen University School of Computer Science Guangdong510006 China

Time series forecasting, aiming to learn models from historical data and predict future values in time series, is a fundamental research topic in machine learning. However, few efforts have been devoted to addressing the confounding effects in time series data, e.g., the historical data are affected by some hidden surrounding factors (i.e., confounders), leading to biased forecasting models for future data. This paper presents a causal intervention approach to eliminate the bias that is raised by some hidden confounders. By using a causal graph, we illustrate why hidden confounders can bring bias in time series forecasting and how to tackle it. We implement causal intervention by a deep architecture that consists of two modules, a Confounders Estimation module to estimate the hidden confounders and a Debiasing module to eliminate the confounding bias in the forecasting model via sampling on confounders. We conduct comprehensive evaluations on various time series datasets. The experiment results indicate that the proposed method can reduce the negative confounding effects in time series data, and it achieves superior gains over state-of-the-art baselines for time series forecasting. © 1989-2012 IEEE.

关键词： Prediction models

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LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge

LLDB: Efficient Low-Light Image Enhancement with Difffusion ...

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

作者： Ma, Junlong Yue, Conghan Peng, Zhengwei Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://***/M-Chase/LLDB. © 2025 IEEE.

关键词： Diffusion Bridge Diffusion Model Low-Light image Enhancement

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SymGaussian: Occluded Human Rendering with Multi-scale Symmetry Feature from Monocular Video

SymGaussian: Occluded Human Rendering with Multi-scale Symme...

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

作者： Jiang, Zekai Duan, Tong Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

The growing demand for high-quality 3D human rendering in real-world applications highlights significant challenges. These challenges are particularly evident in dealing with occlusion in monocular video. Previous methods often rely on controlled datasets and overlook the inherent symmetry of the human body, leading to incomplete rendering in occluded areas. To address these limitations, we propose SymGaussian, a novel Gaussian Splatting-based approach for rendering occluded human from monocular video. We introduce Multi-scale Symmetry Feature to compensate for lost information in occluded areas, along with a Projective Texture Mapping method that efficiently encodes 2D appearance while preserving 3D perception. Experiments show that SymGaussian outperforms state-of-the-art methods in rendering quality, while achieving rapid training speed and real-time rendering capability exceeding 200 FPS. © 2025 IEEE.

关键词： 3D Gaussian Splatting Multi-scale Symmetry Feature Occluded Human Rendering Texture Mapping

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Anima2: Cross-Species Animal Animation through Image-to-Video Synthesis with Subject Alignment

Anima2: Cross-Species Animal Animation through Image-to-Vide...

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

作者： Xu, Yuanfeng Chen, Yuhao Huang, Zhongzhan He, Zijian Wang, Guangrun Lin, Liang Sun Yat-sen University China Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350368741

Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differ greatly from that of a pig due to their distinct body structures). In this paper, we present Anima2, a zero-shot diffusion-based video generator to address this issue, aiming to accurately ANIMAte ANIMAls while preserving the background. The key technique involves two-fold subject alignment. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. They capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing animals by introducing a scale-information remover and an adaptive rescaling module. They both enhance subject alignment for accurate cross-species animation. Additionally, we introduce two high-quality animal video datasets with diverse species to benchmark cross-species animation. Trained on these extensive datasets, our model directly generates videos with accurate movements, consistent appearances, and high-fidelity frames, eliminating the need for test-time training. Extensive experiments demonstrate our method's superiority in cross-species animation, showcasing robust adaptability and generality. © 2025 IEEE.

关键词： cross-species subject alignment video editing

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Learning Transition Patterns by Large Language Models for Sequential Recommendation 31

Learning Transition Patterns by Large Language Models for Se...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhai, Jianyang Mai, Zi-Feng Zheng, Dongyi Wang, Chang-Dong Zheng, Xiawu Li, Hui Yang, Feidiao Tian, Yonghong Sun Yat-sen University China Pengcheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China School of Informatics Xiamen University China Peking University China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have demonstrated powerful performance in sequential recommendation due to their robust language modeling and comprehension capabilities. In such paradigms, the item texts of interaction sequences are formulated as sentences, and LLMs are utilized to learn language representations or directly generate target item texts by incorporating instructions. Despite their promise, these methods solely focus on modeling the mapping from sequential texts to target items, neglecting the relationship between the items in an interaction sequence. This results in a failure to learn the transition patterns between items, which reflect the dynamic change in user preferences and are crucial for predicting the next item. To tackle this issue, we propose a novel framework for mapping the sequential item texts to the sequential item IDs, named ST2SI. Specifically, we first introduce multi-query input and item linear projection (ILP) to model the conditional probability distribution of items. Then, we further propose ID alignment to address misalignment between item texts and item IDs by instruction tuning. Finally, we propose efficient ILP tuning to adapt flexibly to different scenarios, requiring only training a linear layer to achieve competitive performance. Extensive experiments on six real-world datasets show that our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10, and 8.42% in MRR. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Edge-Cloud Cooperation-Driven Intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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SymGaussian: Occluded Human Rendering with Multi-scale Symmetry Feature from Monocular Video

SymGaussian: Occluded Human Rendering with Multi-scale Symme...

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

作者： Zekai Jiang Tong Duan Dongyu Zhang Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Solid modeling Three-dimensional displays Streaming media Signal processing Rendering (computer graphics) Real-time systems Acoustics Speech processing

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LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge

LLDB: Efficient Low-Light Image Enhancement with Difffusion ...

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

作者： Junlong Ma Conghan Yue Zhengwei Peng Dongyu Zhang Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Bridges Computational modeling Noise reduction Diffusion processes Speech enhancement Signal processing Diffusion models Image restoration Image enhancement Faces

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Enhanced Control for Diffusion Bridge in Image Restoration

Enhanced Control for Diffusion Bridge in Image Restoration

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

作者： Conghan Yue Zhengwei Peng Junlong Ma Dongyu Zhang Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Image restoration refers to the process of restoring a damaged low-quality image back to its corresponding high-quality image. Recently, a special type of diffusion bridge model has achieved more advanced results in image restoration. It can transform the direct mapping from low-quality to high-quality images into a diffusion process, restoring low-quality images through a reverse process. However, the current diffusion bridge restoration models do not emphasize the idea of conditional control, which may affect performance. This paper introduces the ECDB model enhancing the control of the diffusion bridge with low-quality images as conditions. Moreover, in response to the characteristic of diffusion models having low denoising level at larger values of t, we also propose a Conditional Fusion Schedule, which more effectively handles the conditional feature information of various modules. Experimental results prove that the ECDB model has achieved state-of-the-art results in many image restoration tasks, including deraining, inpainting and super-resolution. Code is avaliable at https://***/Hammour-steak/ECDB.

关键词： Bridges Schedules Noise reduction Superresolution Diffusion processes Transforms Predictive models Feature extraction Image restoration Speech processing

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Anima2: Cross-Species Animal Animation through Image-to-Video Synthesis with Subject Alignment

Anima2: Cross-Species Animal Animation through Image-to-Vide...

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

作者： Yuanfeng Xu Yuhao Chen Zhongzhan Huang Zijian He Guangrun Wang Liang Lin Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China Peng Cheng Laboratory China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differ greatly from that of a pig due to their distinct body structures). In this paper, we present Anima 2 , a zero-shot diffusion-based video generator to address this issue, aiming to accurately ANIMAte ANIMAls while preserving the background. The key technique involves two-fold subject alignment. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. They capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing animals by introducing a scale-information remover and an adaptive rescaling module. They both enhance subject alignment for accurate cross-species animation. Additionally, we introduce two high-quality animal video datasets with diverse species to benchmark cross-species animation. Trained on these extensive datasets, our model directly generates videos with accurate movements, consistent appearances, and high-fidelity frames, eliminating the need for test-time training. Extensive experiments demonstrate our method’s superiority in cross-species animation, showcasing robust adaptability and generality.

关键词： Training Accuracy Laplace equations Animals Shape Signal processing Animation Feature extraction Information leakage Speech processing

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