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2021 2nd International Symposium on Automation, information and Computing, ISAIC 2021

作者： Li, Yiqun Huang, Guimin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

As a universal language, English has been paid more and more attention, among which oral English learning is very important. In this paper, the two key technologies of pronunciation error detection and quality evaluation are studied, both of them are effectively integrated, aiming to build a model for L2 learners' English pronunciation quality evaluation. This paper mainly studies two different methods of pronunciation error detection. Based on the speech recognition framework, the standard score is compared with the threshold to judge the correctness of phoneme pronunciation, and the phoneme-dependent threshold is set to improve the maximum Precision to 0.44. By judging the correct pronunciation and confusing phoneme, the accuracy of pronunciation error detection is improved to 81.26%. This paper proposes the fusion algorithm from multi-dimensions of speech fluency and intonation respectively, and a newly designed feature called word duration ratio, which significantly improve the correlation of pronunciation quality evaluation to 0.746. © Published under licence by IOP Publishing Ltd.

关键词： Speech recognition

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UltraEdit: instruction-based fine-grained image editing at scale 24

UltraEdit: instruction-based fine-grained image editing at s...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Haozhe Zhao Xiaojian Ma Liang Chen Shuzheng Si Rujie Wu Kaikai An Peiyu Yu Minjia Zhang Qing Li Baobao Chang National Key Laboratory for Multimedia Information Processing Peking University and School of Software and Microelectronics Peking University State Key Laboratory of General Artificial Intelligence BIGAI National Key Laboratory for Multimedia Information Processing Peking University and School of Computer Science Peking University China Department of Computer Science and Technology Tsinghua University School of Computer Science Peking University China UCLA UIUC National Key Laboratory for Multimedia Information Processing Peking University

ISBN: (纸本)9798331314385

This paper presents UltraEdit, a large-scale (~4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix [10] and MagicBrush [71], and provide a systematic approach to producing massive and high-quality image editing samples. UltraEdit offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters; 2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models; 3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models are available in ***/pkunlp-icler/UltraEdit.

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Chinese image captioning via double decoding based on visual prompts 5

Chinese image captioning via double decoding based on visual...

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5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Tan, Weiguo Xiao, Yannan Wen, Yayuan Deng, Zhenrong Huang, Wenming School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510682863

The significant progress of vision-language pre-trained models (VLMs) and large language models (LLMs) have provided a feasible new mode for image captioning, which relies on VLMs to process images and then utilizes LLMs to generate captions, simplifying the caption generation process and making it lighter. Based on this mode, to address the issues of deviation between the generated captions and the content of image expressions, incomplete descriptive information, and resource hungry in previous Chinese image captioning models, we propose a Chinese image captioning model via double decoding based on Visual Prompts, DDVP. This model employs the CLIP model as the encoder, the GPT2 model as the decoder, and introduces Visual Prompts, which are keywords related to image content. The model adopts a double decoding approach, first decoding to generate Visual Prompts and then decoding to generate the final captions based on the Visual Prompts. Through evaluation, we have demonstrated that our model achieves competitive results on the AIC-ICC dataset, and while maintaining fluency, the generated captions of DDVP can also cover the information in the image more comprehensively and accurately. © 2024 SPIE.

关键词： Visual languages

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OpenViewer: Openness-Aware Multi-View Learning 39

OpenViewer: Openness-Aware Multi-View Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)157735897X

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Optical polarized orthogonal matrix

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Photonics Research 2025年第2期13卷 373-381页

作者： SHUJUN ZHENG JIAREN TAN XIANMIAO XU HONGJIE LIU YI YANG XIAO LIN XIAODI TAN Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhou 350117China Department of Electrical and Computer Engineering Duke UniversityDurhamNorth Carolina 27708USA College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China

Multiplexing technology serves as an effective approach to increase both information storage and transmission ***,when exploring multiplexing methods across various dimensions,the polarization dimension encounters limitations stemming from the finite orthogonal *** that only two mutually orthogonal polarizations are identifiable on the basic Poincarésphere,this poses a hindrance to polarization *** overcome this challenge,we propose a construction method for the optical polarized orthogonal matrix(OPOM),which is not constrained by the number of orthogonal ***,we experimentally validate its application in high-dimensional multiplexing of polarization *** explore polarization holography technology,capable of recording amplitude,phase,and polarization,for the purpose of recording and selective reconstruction of polarization *** research reveals that,despite identical polarization states,multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations,owing to the orthogonal selectivity among information.

关键词： two mutually orthogonal polarizations optical polarized orthogonal matrix construction method multiplexing methods polarization dimension multiplexing orthogonal polarization combinations polarization holography

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Improved forecasting via physics-guided machine learning as exemplified using“21·7”extreme rainfall event in Henan

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Science China Earth Sciences 2024年第5期67卷 1652-1674页

作者： Qi ZHONG Zhicha ZHANG Xiuping YAO Shaoyu HOU Shenming FU Yong CAO Linguo JING China Meteorological Administration Training Center Beijing 100081China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Zhejiang Meteorological Observatory Hangzhou 310017China Hebei Provincial Weather Modification Center Shijiazhuang 050020China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China National Meteorological Center Beijing 100081China School of Computer Science Chengdu University of Information TechnologyChengdu 610225China

As a natural disaster,extreme precipitation is among the most destructive and influential,but predicting its occurrence and evolution accurately is very challenging because of its rarity and *** the example of the“21·7”extreme precipitation event(17–21 July 2021)in Henan Province,this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme *** physics-guided ways of embedding physical features,fusing physical model forecasts and revised loss function are used,i.e.,(1)analyzing the anomalous circulation and thermodynamical factors,(2)analyzing the multi-model forecast bias and the associated underlying reasons for it,and(3)using professional forecasting knowledge to design the loss function,and the corresponding results are used as input for machine learning to improve the forecasting *** results indicate that by learning the relationship between anomalous physical features and heavy precipitation,the forecasting of precipitation intensity is improved significantly,but the location is rarely adjusted and more false alarms *** reasons for this are as *** anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation;moreover,the samples of extreme precipitation are sparse and so the algorithm used here is ***,by combining“good and different”multi models with machine learning,the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved ***,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is ***,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,

关键词： Extreme precipitation event Refined assessment Anomalous physical features Multi-model fusion Machine learning

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STDNet: Spatio-Temporal Decomposed Network for Video Grounding

STDNet: Spatio-Temporal Decomposed Network for Video Groundi...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Xu, Yuanwu Zhang, Yuejie Feng, Rui Zhao, Rui-Wei Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing China Academy for Engineering and Technology Fudan University China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai Key Laboratory of Financial Information Technology China School of Information Technology Deakin University Australia

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Previous methods for video grounding treated either the query or the video as a whole, while neglecting their respective semantics in the orthogonal space and time dimensions. Since spatial semantics appears frequently in a video, temporal semantics is more discriminative and deserves more attention. Based on such considerations, we propose a novel Spatio-Temporal Decomposed Network (STDNet) which decomposes the query and the video into their spatial and temporal semantics, respectively. Specifically, spatial and temporal words are selected from the query, and the video is split into two pathways. Spatial cross-modal attention is computed first and serves as prior knowledge for temporal attention. A new localization strategy is also devised which regresses the segment's start conditioned on the end and essentially breaks the independence assumption made in previous methods. Experimental results on three public benchmark datasets show that our STDNet outperforms the state-of-the-art methods. © 2022 IEEE.

关键词： Semantics

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Semantic-Driven Saliency-Context Separation for Video Captioning

Semantic-Driven Saliency-Context Separation for Video Captio...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Jing, Heming Zhang, Yuejie Feng, Rui Zhao, Rui-Wei Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Fudan University Academy for Engineering and Technology China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics China School of Information Technology Deakin University Australia

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Video captioning aims at generating a natural language de-scription for a given video clip including not only salient sce-narios but also contextual scenarios. The former reveal the highlight of a video and are usually the focus of most existing captioning methods. The latter, however, are not well ex-plored and even ignored easily, though they may provide cer-tain detailed and latent information that can help with a better understanding of the video. To effectively exploit the infor-mation contained in both, a novel video captioning network is proposed. It has two key modules: Cross-Modality Selection (CMS) and Saliency-Context Adaptive Decoder (SCAD). Specifically, CMS mainly focuses on utilizing the semantic information to distinguish saliency and context. Meanwhile, SCAD adaptively identifies both the saliency and context to generate more detailed and precise captions. Experiments on two benchmark datasets, i.e., MSVD and MSR-VTT, demon-strate the effectiveness of our model through the comparison with state-of-the-art methods. © 2022 IEEE.

关键词： Semantics

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Phonon anharmonicity and thermal conductivity of two-dimensional van der Waals materials: A review

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Science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 36-45页

作者： Xuefei Yan Bowen Wang Yulong Hai Devesh RKripalani Qingqing Ke Yongqing Cai Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials EngineeringUniversity of MacaoMacao 999078China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Microelectronics Science and Technology Sun Yat-sen UniversityZhuhai 519082China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen UniversityZhuhai 519082China

Two-dimensional(2D) van der Waals(vdW) materials have extraordinary thermal properties due to the effect of quantum confinement, making them promising for thermoelectric energy conversion and thermal management in microelectronic *** this review, the mechanism of phonon anharmonicity originating from three-and four-phonon interactions is derived. The phonon anharmonicity of 2D vdW materials, involving the Grüneisen parameter, phonon lifetime, and thermal conductivity, is summarized and derived in detail. The size-dependent thermal conductivity of representative 2D vdW materials is discussed experimentally and theoretically. This review will present fundamental and advanced knowledge on how to evaluate the phonon anharmonicity in 2D vdW materials, which will aid the design of new structures and materials for applications related to energy transfer and conversion.

关键词： van der Waals materials two-dimensional phonon anharmonicity thermal conductivity

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