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摘要 ：
The visual quality of 3D-synthesized videos is closely related to the development and broadcasting of immersive media such as free-viewpoint videos and six degrees of freedom navigation. Therefore, studying the 3D-Synthesized video quality assessment is helpful to promote the popularity of immersive media applications. Inspired by the texture compression, depth compression and virtual view synthesis polluting the visual quality of 3D-synthesized videos at pixel-, structure-and content-levels, this paper proposes a Multi-Level 3D-Synthesized Video Quality Assessment algorithm, namely ML-SVQA, which consists of a quality feature perception module and a quality feature regression module. Specifically, the quality feature perception module firstly extracts motion vector fields of the 3D-synthesized video at pixel-, structure-and content-levels by combining the perception mechanism of human visual system. Then, the quality feature perception module measures the temporal flicker distortion intensity in the no-reference environment by calculating the self-similarity of adjacent motion vector fields. Finally, the quality feature regression module uses the machine learning algorithm to learn the mapping of the developed quality features to the quality score. Experiments constructed on the public IRCCyN/IVC and SIAT synthesized video datasets show that our ML-SVQA is more effective than state-of-the-art image/video quality assessment methods in evaluating the quality of 3D-Synthesized videos.
关键词 ：
multi-level multi-level 3D-synthesized video 3D-synthesized video Video quality assessment Video quality assessment temporal flicker distortion temporal flicker distortion
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| GB/T 7714 | Wang, Guangcheng , Huang, Baojin , Gu, Ke et al. No-Reference Multi-Level Video Quality Assessment Metric for 3D-Synthesized Videos [J]. | IEEE TRANSACTIONS ON BROADCASTING , 2024 . |
| MLA | Wang, Guangcheng et al. "No-Reference Multi-Level Video Quality Assessment Metric for 3D-Synthesized Videos" . | IEEE TRANSACTIONS ON BROADCASTING (2024) . |
| APA | Wang, Guangcheng , Huang, Baojin , Gu, Ke , Liu, Yuchen , Liu, Hongyan , Shi, Quan et al. No-Reference Multi-Level Video Quality Assessment Metric for 3D-Synthesized Videos . | IEEE TRANSACTIONS ON BROADCASTING , 2024 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Weakly supervised group activity recognition deals with the dependence on individual-level annotations during understanding scenes involving multiple individuals, which is a challenging task. Existing methods either take the trained detectors to extract individual features or utilize the attention mechanisms for partial context encoding, followed by integration to form the final group-level representations. However, the detectors require individual-level annotations during the training phase and have a mis-detection issue, and the partial contexts extracted immediately from the whole complex scene are too ambiguous without the guidance of concrete semantics. In this article, we investigate the hierarchical structure inherent in group-level labels to extract the fine-grained semantics without using detectors for weakly supervised group activity recognition. A multi-hot encoding strategy combined with a semantic encoder is first adopted to get the label semantics embeddings. The semantic and visual scene information are then fused through a semantic decoder to obtain activity-specific features. Lastly, we employ the multi-label classification and integrate the scores of hierarchical activity labels. Experimental results show that our proposed method achieves the state-of-the-art performance on three benchmarks, and the accuracy on the Volleyball dataset exceeds the second-best method by 2%.
关键词 ：
Weakly Supervised Group Activity Recognition Weakly Supervised Group Activity Recognition Label Semantics Label Semantics Multi-Label Classification Multi-Label Classification
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| GB/T 7714 | Wu, Lifang , Tian, Meng , Xiang, Ye et al. Learning Label Semantics for Weakly Supervised Group Activity Recognition [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 6386-6397 . |
| MLA | Wu, Lifang et al. "Learning Label Semantics for Weakly Supervised Group Activity Recognition" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 6386-6397 . |
| APA | Wu, Lifang , Tian, Meng , Xiang, Ye , Gu, Ke , Shi, Ge . Learning Label Semantics for Weakly Supervised Group Activity Recognition . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 6386-6397 . |
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摘要 ：
Underwater image quality assessment (UIQA) plays a crucial role in monitoring and detecting the quality of acquired underwater images in underwater imaging systems. Currently, the investigation of UIQA encounters two major challenges. First, a lack of large-scale UIQA databases for benchmarking UIQA algorithms remains, which greatly restricts the development of UIQA research. The other limitation is that there is a shortage of effective UIQA methods that can faithfully predict underwater image quality. To alleviate these two challenges, in this paper, we first construct a large-scale UIQA database (UIQD). Specifically, UIQD contains a total of 5369 authentic underwater images that span abundant underwater scenes and typical quality degradation conditions. Extensive subjective experiments are executed to annotate the perceived quality of the underwater images in UIQD. Based on an in-depth analysis of underwater image characteristics, we further establish a novel baseline UIQA metric that integrates channel and spatial attention mechanisms and a transformer. Channel- and spatial attention modules are used to capture the image channel and local quality degradations, while the transformer module characterizes the image quality from a global perspective. Multilayer perception is employed to fuse the local and global feature representations and yield the image quality score. Extensive experiments conducted on UIQD demonstrate that the proposed UIQA model achieves superior prediction performance compared with the state-of-the-art UIQA and IQA methods.
关键词 ：
Image color analysis Image color analysis Image quality Image quality Attention mechanism Attention mechanism Transformers Transformers Degradation Degradation Imaging Imaging image database image database underwater image underwater image Databases Databases transformer transformer Measurement Measurement image quality assessment (IQA) image quality assessment (IQA)
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| GB/T 7714 | Liu, Yutao , Zhang, Baochao , Hu, Runze et al. Underwater Image Quality Assessment: Benchmark Database and Objective Method [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 7734-7747 . |
| MLA | Liu, Yutao et al. "Underwater Image Quality Assessment: Benchmark Database and Objective Method" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 7734-7747 . |
| APA | Liu, Yutao , Zhang, Baochao , Hu, Runze , Gu, Ke , Zhai, Guangtao , Dong, Junyu . Underwater Image Quality Assessment: Benchmark Database and Objective Method . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 7734-7747 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Due to the light absorption and scattering in waterbodies, acquired underwater images frequently suffer from color cast, blur, low contrast, noise, etc., which seriously degrade the image quality and affect their subsequent applications. Therefore, it is necessary to propose a reliable and practical underwater image quality assessment (IQA) model that can faithfully evaluate underwater image quality. To this end, in this article, we establish a novel quality assessment model for underwater images by in-depth analysis and characterization of multiple image properties. Specifically, we propose characterizing the image luminance, color cast, sharpness, contrast, fog density and noise to comprehensively describe the image quality to evaluate the underwater image quality more accurately. Dedicated features are elaborately investigated to characterize those quality-aware image properties. After feature extraction, we employ support vector regression (SVR) to integrate all the quality-aware features and regress them onto the underwater image quality score. Extensive tests performed on standard underwater image quality databases demonstrate the superior prediction performance of the proposed underwater IQA model to state-of-the-art congeneric quality assessment models.
关键词 ：
Underwater image Underwater image objective metric objective metric Image color analysis Image color analysis Colored noise Colored noise Predictive models Predictive models no-reference (NR) no-reference (NR) Visualization Visualization image quality assessment (IQA) image quality assessment (IQA) Feature extraction Feature extraction Image quality Image quality statistical modeling statistical modeling Indexes Indexes
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| GB/T 7714 | Liu, Yutao , Gu, Ke , Cao, Jingchao et al. UIQI: A Comprehensive Quality Evaluation Index for Underwater Images [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 2560-2573 . |
| MLA | Liu, Yutao et al. "UIQI: A Comprehensive Quality Evaluation Index for Underwater Images" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 2560-2573 . |
| APA | Liu, Yutao , Gu, Ke , Cao, Jingchao , Wang, Shiqi , Zhai, Guangtao , Dong, Junyu et al. UIQI: A Comprehensive Quality Evaluation Index for Underwater Images . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 2560-2573 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Existing group activity recognition methods generally use optical flow image to represent motion within videos, which often struggle to capture the movements of individuals inaccurately. In this paper, we explore the effectiveness of more kinds of motion information for group activity recognition. We propose a novel multi- scale MOtion-based relational reasoning framework for Group Activity Recognition (MOGAR). It combines joint motion (intra-individual level) with trajectory (individual-level) and individual position (inter-individual level) to acquire richer activity representation. Specifically, it involves two branches: the trajectory branch utilizes individuals' trajectories and positions to extract the motion feature at the individual and inter-individual levels. The joint branch extracts the motion features at the intra-individual level. Furthermore, the gated recurrent units (GRU) and Transformers are employed to enhance the corresponding features through gating mechanism and self-attention mechanism. The features from the two branches are concatenated for group activity recognition. The experiments on two public datasets demonstrate that our method achieves competitive performance and has potential benefits in terms of computational complexity.
关键词 ：
Group activity recognition Group activity recognition Dual-branch network Dual-branch network Trajectory/position encoding module Trajectory/position encoding module Multi-scale motion information Multi-scale motion information
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| GB/T 7714 | Zheng, Yihao , Wang, Zhuming , Gu, Ke et al. Multi-scale motion-based relational reasoning for group activity recognition [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 139 . |
| MLA | Zheng, Yihao et al. "Multi-scale motion-based relational reasoning for group activity recognition" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 139 (2024) . |
| APA | Zheng, Yihao , Wang, Zhuming , Gu, Ke , Wu, Lifang , Li, Zun , Xiang, Ye . Multi-scale motion-based relational reasoning for group activity recognition . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 139 . |
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摘要 ：
一种VOCs燃烧残留量精确检测方法属于智能环保领域。本发明步骤：基于计算流体力学，针对双股蒸汽助燃型火炬进行仿真建模，构建放空火炬系统最终生成混合气体成分的仿真数据集，基于烟气分析仪测量放空火炬系统混合气体成分构建测量数据集；针对因仪器检测过程耗时导致测量数据集存在时间滞后的问题，采用延迟消除方法修正数据集中的VOCs燃烧残留量的时间戳，实现VOCs燃烧残留量的预测；基于构建数据集使用RBF网络建立放空火炬VOCs燃烧残留量预测模型；针对RBF网络的设计，设计基于密度的Canopy‑K均值算法初始化网络的结构和参数，提高网络性能；采用微调和基于梯度的算法调整RBF网络参数，提高网络的逼近能力。
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| GB/T 7714 | 郭楠 , 乔俊飞 , 顾锞 et al. 一种VOCs燃烧残留量精确检测方法 : CN202310714365.X[P]. | 2023-06-15 . |
| MLA | 郭楠 et al. "一种VOCs燃烧残留量精确检测方法" : CN202310714365.X. | 2023-06-15 . |
| APA | 郭楠 , 乔俊飞 , 顾锞 , 李鹏宇 , 武利 , 贾丽杰 et al. 一种VOCs燃烧残留量精确检测方法 : CN202310714365.X. | 2023-06-15 . |
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摘要 ：
本发明公开了一种面向污染监测的多通路深度神经网络高效训练方法，其中多通路深度神经网络高效训练方法先通过分步筛选集成所有单通道神经网络及其融合部分的最优参数，再对融合后的多通路深度神经网络进行微调得到污染监测模型的最优参数，将污染物图像样本输入网络进行训练，能有效提高污染监测模型精度。本发明针对不同子网络及其组合进行网络训练，集成了所有子网络及其融合部分的最优参数，解决了随机初始化参数容易使网络陷入局部最小值的问题，从而提高了神经网络模型监测精度；提高污染监测模型的监测精度。
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| GB/T 7714 | 顾锞 , 谢双憶 , 刘静 . 一种面向污染监测的多通路深度神经网络高效训练方法 : CN202310106807.2[P]. | 2023-02-13 . |
| MLA | 顾锞 et al. "一种面向污染监测的多通路深度神经网络高效训练方法" : CN202310106807.2. | 2023-02-13 . |
| APA | 顾锞 , 谢双憶 , 刘静 . 一种面向污染监测的多通路深度神经网络高效训练方法 : CN202310106807.2. | 2023-02-13 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose major challenges to the corresponding content quality assessment, which plays a critical role to ensure and improve the final user-perceived quality of experience (QoE) in various screen content communication and networking systems. Quality assessment of such screen content has attracted much attention recently, primarily because the screen content grows explosively due to the prevalence of cloud and remote computing applications in recent years, and due to the fact that conventional quality assessment methods can not handle such content effectively. As the most technology-oriented part of QoE modeling, image/video content/media quality assessment has drawn wide attention from researchers, and a large amount of work has been carried out to tackle the problem of screen content quality assessment. This article is intended to provide a systematic and timely review on this emerging research field, including (1) background of natural scene vs. screen content quality assessment; (2) characteristics of natural scene vs. screen content; (3) overview of screen content quality assessment methodologies and measures; (4) relevant benchmarks and comprehensive evaluation of the state-of-the-art; (5) discussions on generalizations from screen content quality assessment to QoE assessment, and other techniques beyond QoE assessment; and (6) unresolved challenges and promising future research directions. Throughout this article, we focus on the differences and similarities between screen content and conventional natural scene content. We expect that this review article shall provide readers with an overview of the background, history, recent progress, and future of the emerging screen content quality assessment research.
关键词 ：
quality of experience quality of experience Screen content Screen content natural scene natural scene quality assessment quality assessment
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| GB/T 7714 | Min, Xiongkuo , Gu, Ke , Zhai, Guangtao et al. Screen Content Quality Assessment: Overview, Benchmark, and Beyond [J]. | ACM COMPUTING SURVEYS , 2022 , 54 (9) . |
| MLA | Min, Xiongkuo et al. "Screen Content Quality Assessment: Overview, Benchmark, and Beyond" . | ACM COMPUTING SURVEYS 54 . 9 (2022) . |
| APA | Min, Xiongkuo , Gu, Ke , Zhai, Guangtao , Yang, Xiaokang , Zhang, Wenjun , Le Callet, Patrick et al. Screen Content Quality Assessment: Overview, Benchmark, and Beyond . | ACM COMPUTING SURVEYS , 2022 , 54 (9) . |
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摘要 ：
一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法，属于智能环保技术领域。本发明所述方法包括以下步骤：基于计算流体力学，针对双股蒸汽助燃型火炬进行仿真建模，获得废气成分流速、助燃蒸汽量和燃尽率数据；根据仿真数据使用LSTM网络建立放空火炬燃烧状态预测模型；采用NSGA‑Ⅲ算法，对放空火炬所需最佳助燃蒸汽量进行寻优；将优化算法改进为动态优化算法。本发明通过在软件中仿真实际工况获得大量可靠数据解决了实际放空火炬数据稀缺问题，通过神经网络建模解决了仿真模型计算过慢、难以实际应用的问题，通过优化算法解决能耗与燃烧状态之间的耦合关系，能够快速准确地定量判断放空火炬燃烧状态和所需助燃蒸汽量。
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| GB/T 7714 | 乔俊飞 , 彭益新 , 郭楠 et al. 一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法 : CN202211129496.3[P]. | 2022-09-15 . |
| MLA | 乔俊飞 et al. "一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法" : CN202211129496.3. | 2022-09-15 . |
| APA | 乔俊飞 , 彭益新 , 郭楠 , 顾锞 . 一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法 : CN202211129496.3. | 2022-09-15 . |
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摘要 ：
本发明公开了一种基于回声状态网络的放空火炬燃烧状态精确控制方法，该方法基于回声状态网络的模型预测控制技术、设定值跟踪控制技术，利用Fluent软件对放空火炬的湍动燃烧过程进行模拟，计算其燃尽率和破坏去除率来精确判断燃烧状态，然后根据公式计算出精确的助燃蒸汽流量，从而对助燃蒸汽流量进行精确调控以实现高效燃烧。本发明通过建立放空火炬机理模型，筛选出高质量的数据建立回声状态网络模型，并预测最佳助燃蒸汽流量，随后设计回声状态网络辨识器和预测控制器，对助燃蒸汽流量进行设定值在线跟踪控制。基于设定值跟踪控制研究，可及时地校正控制过程中出现的各种复杂情况，在火炬高效燃烧和节约资源方面都提升了很多。
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| GB/T 7714 | 乔俊飞 , 刘佳晖 , 郭楠 et al. 一种基于回声状态网络的放空火炬燃烧状态精确控制方法 : CN202211119832.6[P]. | 2022-09-15 . |
| MLA | 乔俊飞 et al. "一种基于回声状态网络的放空火炬燃烧状态精确控制方法" : CN202211119832.6. | 2022-09-15 . |
| APA | 乔俊飞 , 刘佳晖 , 郭楠 , 顾锞 . 一种基于回声状态网络的放空火炬燃烧状态精确控制方法 : CN202211119832.6. | 2022-09-15 . |
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