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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Take a Prior From Other Tasks for Severe Blur Removal

SSRN

引用

SSRN 2023年

作者： Wang, Pei Zhu, Yu Xue, Danna Yan, Qingsen Sun, Jinqiu Yoon, Sung-Eui Zhang, Yanning School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain School of Astronautics Northwestern Polytechnical University Xi’an China School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

Recovering clear structures from severely blurry inputs is a huge challenge, especially when camera– to-scene movements are significant. While segmentation maps can help to deblur human face images, their effectiveness is limited in complex natural scenes. To solve this problem, we propose utilizing high-level vision tasks, such as classification, to learn a comprehensive prior for severe blur removal. We propose a feature learning strategy based on knowledge distillation, which aims to learn the priors with global contexts and sharp local structures. To integrate the priors effectively, we propose a semantic prior embedding layer with multi-level aggregation and semantic attention. We validate our method on natural image deblurring benchmarks by introducing the priors to various models, including UNet and mainstream deblurring baselines, to demonstrate its effectiveness and generalization ability. The results show that our approach outperforms existing methods on severe blur removal with our plug-and-play semantic priors. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges

arXiv

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arXiv 2024年

作者： Cao, Yuchen Dai, Jianglai Wang, Zhongyan Zhang, Yeyubei Shen, Xiaorui Liu, Yunchong Tian, Yexin Khoury College of Computer Science Northeastern University United States Department of EECS University of California Berkeley United States Center for Data Science New York University United States School of Engineering and Applied Science University of Pennsylvania United States Georgia Institute of Technology College of Computing United States

The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through user-generated content. This systematic review examines machine learning (ML) models for detecting mental illness, with a particular focus on depression, using social media data. It highlights biases and methodological challenges encountered throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. The review reveals significant biases affecting model reliability and generalizability. A predominant reliance on Twitter (63.8%) and English-language content (over 90%) limits diversity, with most studies focused on users from the United States and Europe. Non-probability sampling methods (approximately 80%) limit representativeness. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning was observed, with only 27.7% properly tuning models. About 17% did not adequately partition data into training, validation, and test sets, risking overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing protocols, ensure consistent model development practices, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally. © 2024, CC BY.

关键词： Adversarial machine learning

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A Review of Super Resolution technology for Extended-Range Precipitation Forecasting

A Review of Super Resolution Technology for Extended-Range P...

引用

International Conference on computer and Applications (ICCA)

作者： Jiahan Chen Mingxuan Mao Chengsheng Yuan Yuanzhi Zhong Zhenfei Tang Department of Computing The Hong Kong Polytechnic University Hong Kong China School of Automation Wuxi University Wuxi China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Fujian Huawang Information Technology Co. Ltd. Fujian China Fujian Provincial Climate Center Fujian China

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

In the field of meteorology, precipitation holds significant importance for human activities and environment. Extended-range precipitation forecasting, covering a timeframe of weeks to months, is crucial across various sectors, including water resource management, agriculture, and disaster prevention. However, the conventional meteorological forecasting models often fail to satisfy the precise demand for accurate and detailed precipitation predictions due to the inherent limitations in resolution and data collection frequency associated with these methods. Recently, super resolution technology has emerged as a promising solution with the potential to transform the resolution of meteorological data. By leveraging advanced algorithms and computational prowess, this technology can generate high-resolution images from low-resolution inputs. In this comprehensive review, we meticulously dissect the state-of-the-art super resolution techniques utilized in extended-range precipitation forecasting tasks. By classifying and analyzing the literature based on various methodologies, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, we uncover their performance characteristics, strengths, and limitations. Our in-depth exploration reveals that while these methods have shown promise, they also face significant challenges. For example, CNNs struggle with capturing ultrafine details, GANs exhibit training instability, and diffusion models require further optimizations in terms of their computational efficiency. Future research efforts should focus on enhancing model architectures, seamlessly incorporating physical constraints to ensure the physical consistency of the generated data, and optimizing the current data utilization strategies. Furthermore, it is crucial to develop comprehensive evaluation metrics, conduct extensive long-term and regional validation studies, and establish effective uncertainty quantification metho

关键词： Precipitation Uncertainty Reviews Prevention and mitigation Superresolution Diffusion models Data models Forecasting Water resources Faces

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

引用

Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline

arXiv

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arXiv 2023年

作者： Wu, Xuecheng Sun, Heli Xue, Junxiao Nie, Jiayu Kong, Xiangyan Zhai, Ruofan He, Liang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Research Center for Space Computing System Zhejiang Lab Hangzhou China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Cyber Science and Engineering Zhengzhou University Zhengzhou China

Nowadays, short-form videos (SVs) are essential to web information acquisition and sharing in our daily life. The prevailing use of SVs to spread emotions leads to the necessity of conducting video emotion analysis (VEA) towards SVs. Considering the lack of SVs emotion data, we introduce a large-scale dataset named eMotions, comprising 27,996 videos. Meanwhile, we alleviate the impact of subjectivities on labeling quality by emphasizing better personnel allocations and multi-stage annotations. In addition, we provide the category-balanced and test-oriented variants through targeted data sampling. Some commonly used videos, such as facial expressions, have been well studied. However, it is still challenging to analysis the emotions in SVs. Since the broader content diversity brings more distinct semantic gaps and difficulties in learning emotion-related features, and there exists local biases and collective information gaps caused by the emotion inconsistence under the prevalently audio-visual co-expressions. To tackle these challenges, we present an end-to-end audio-visual baseline AV-CANet which Copyright © 2023, The Authors. All rights reserved.

关键词： Video analysis

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Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs

arXiv

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arXiv 2024年

作者： Chen, Junhao Li, Xiang Ye, Xiaojun Li, Chao Fan, Zhaoxin Zhao, Hao Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China School of Software and Microelectronics Peking University China College of Computer Science Zhejiang University China College of Computer Science and Technology Harbin Engineering University China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don’t fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://***/. © 2024, CC BY-NC-SA.

关键词： 3D modeling

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

引用

2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Retinal-NeRF: Real-time Rendering of Neural Point Fields Across Platform

Retinal-NeRF: Real-time Rendering of Neural Point Fields Acr...

引用

International Conference on Virtual Reality and Visualization (ICVRV)

作者： Mandun Zhang Yuhan Liu Shujia Cheng Yonghui Pang Jiale Shi Zhidong Xiao Xinjiang College of Science & Technology School of Artificial Intelligence Hebei University of Technology Tianjin International Joint Center for Virtual Reality and Visual Computing School of Artificial Intelligence Hebei University of Technology Tianjin China National Centre for Computer Animation Bournemouth University Bournemouth United Kingdom

ISBN: (数字)9798331515447

ISBN: (纸本)9798331515454

Neural Radiance Fields (NeRFs) has emerged as a promising method for 3D reconstruction and novel view synthesis. However, NeRF-based methods rely on implicit encoding and heavy spatial sampling, which differ significantly from the widely used polygon mesh rasterization method. This discrepancy leads to a lack of support from common 3D software and hardware, resulting in inefficient rendering. In this work, we introduce a novel method called Retinal-NeRF, which combines point clouds and signed distance fields, for real-time rendering of 3D scenes. Our goal is achieved by converting NeRF into a representation that can be efficiently processed by standard graphics pipelines. To enhance the geometric accuracy of the scene, we propose two SDF regularization terms to improve the quality of the generated mesh. To ensure real-time rendering, we employ appearance decomposition technique to minimize the size of the MLP within the rendering pipeline. Notably, our findings indicate that the rendering speed of Retinal-NeRF is more than five times faster than existing real-time rendering techniques, without a significant loss in the quality of novel view synthesis.

关键词： Point cloud compression Visualization Solid modeling Three-dimensional displays Pipelines Virtual reality Rendering (computer graphics) Neural radiance field Real-time systems Standards

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