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arXiv 2023年

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Yanan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China ETH Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Vrobotit Lab Beijing University of Posts and Telecommunications China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech China Advanced Research Center KKCompany Taiwan ByteDance China MGTV China Fuzhou University China Shopee Information Technology Co. Ltd. China S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China INSA Rennes CNRS IETR - UMR 6164 Rennes France National Higher School of Telecommunications and ICT Oran Algeria BUAA China Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Learning elastic constitutive material and damping models

arXiv

引用

arXiv 2019年

作者： Wang, Bin Kry, Paul Deng, Yuanmin Ascher, Uri Huang, Hui Chen, Baoquan Advanced Innovation Center for Future Visual Entertainment Beijing Film Academy School of Computer Science McGill University School of Computer Science and Technology Shandong University Department of Computer Science University of British Columbia College of Computer Science and Software Engineering Shenzhen University Advanced Innovation Center for Future Visual Entertainment Beijing Film Academy Peking University

The fidelity of a deformation simulation is highly dependent upon the underlying constitutive material model. Commonly used linear and nonlinear constitutive material models contain many simplifications and only cover a tiny part of possible material behavior. In this work we propose a framework for learning customized models of deformable materials from sparse example surface trajectories. The key idea is to iteratively improve a correction to a nominal model of the elastic and damping properties of the object, which allows new forward simulations with the learned correction to more accurately predict the behavior of a given soft object. The challenge is that such data is sparse as it is typically available only on part of the surface. Sparse reduced space-time optimization identifies gentle control forces with which we extract necessary annotated data for model inference and to finally encapsulate the material correction into a compact parametric form. We demonstrate our method with a set of synthetic examples, as well as with data captured from real world homogeneous elastic objects. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

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Unified quality assessment of in-the-wild videos with mixed datasets training

arXiv

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arXiv 2020年

作者： Li, Dingquan Jiang, Tingting Jiang, Ming National Engineering Laboratory for Video Technology Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China Department of Computer Science Peking University Beijing China Laboratory of Mathematics and Its Applications School of Mathematical Sciences Peking University Beijing China Beijing International Center for Mathematical Research Peking University Beijing China Advanced Innovation Center for Future Visual Entertainment Beijing Film Academy Beijing China

Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents. Moreover, the video contents and distortions among existing datasets are quite different, which leads to poor performance of data-driven methods in the cross-dataset evaluation setting. To improve the performance of quality assessment models, we borrow intuitions from human perception, specifically, content dependency and temporal-memory effects of human visual system. To face the cross-dataset evaluation challenge, we explore a mixed datasets training strategy for training a single VQA model with multiple datasets. The proposed unified framework explicitly includes three stages: relative quality assessor, nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly predict relative quality, perceptual quality, and subjective quality. Experiments are conducted on four publicly available datasets for VQA in the wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental results verify the effectiveness of the mixed datasets training strategy and prove the superior performance of the unified model in comparison with the state-of-the-art models. For reproducible research, we make the PyTorch implementation of our method available at https://***/lidq92/MDTVSFA. Copyright © 2020, The Authors. All rights reserved.

关键词： Video recording

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Health Care Spoken Dialogue System for Diagnostic Reasoning and Medical Product Recommendation

Health Care Spoken Dialogue System for Diagnostic Reasoning ...

引用

IEEE International Conference on Orange Technologies (ICOT)

作者： Bo-Hao Su Shih-Pang Tseng Yu-Shan Lin Jhing-Fa Wang Department of Electrical Engineering National Cheng Kung University Tainan City Taiwan Department of Computer Science and Entertainment Technology Tajen University Pingtung County Taiwan

ISBN: (纸本)9781538673201;9781538673195

We proposed a medical dialogue system based on word embedding and slot filling. After the input speech is converted into text through ASR, the sentences are cut into words through the Jieba word segmentation system. We vectorized the words of the input sentence with the word embedding model, extract information with slot filling based on the cosine similarity, and then the diagnostic reasoning simulation is performed. We adopt the concept of TF-IDF algorithm to train the weight of the symptoms and diseases in our medical database. The more common the disease, the higher the weight. As for symptom, the more the disease has this symptom, the lower the weight of the symptom. After knowing the weight of disease and symptom, we can start to calculate the score of disease and get the most likely disease. Finally, the most suitable product is returned to the user. In the experimental result, the accuracy of slot filling and diagnostic reasoning simulation was 88% and 86% respectively.

关键词： Diseases Cognition Medical diagnostic imaging Filling Databases Natural languages Robots

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Understanding gender equity in author order assignment

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Proceedings of the ACM on Human-computer Interaction 2018年第CSCW期2卷 1–21页

作者： Early, Kirstin Hammer, Jessica Hofmann, Megan Rode, Jennifer A. Wong, Anna Mankoff, Jennifer Yahoo Research 701 First Avenue SunnyvaleCA94089 United States Carnegie Mellon University Human-Computer Interaction Institute Entertainment Technology Center 5000 Forbes Avenue PittsburghPA15213 United States University College London Knowledge Lab Gower Street London United Kingdom Carnegie Mellon University Department of Statistics 5000 Forbes Avenue PittsburghPA15213 United States University of Washington Paul G. Allen School of Computer Science and Engineering 185 E Stevens Way NE SeattleWA98195 United States

Academic success and promotion are heavily infuenced by publication record. In many felds, including computer science, multi-author papers are the norm. Evidence from other felds shows that norms for ordering author names can infuence the assignment of credit. We interviewed 38 students and faculty in human-computer interaction (HCI) and machine learning (ML) at two institutions to determine factors related to assignment of author order in collaborative publication in the feld of computer science. Interview outcomes informed metrics for our bibliometric analysis of gender and collaboration in papers published between 1996 and 2016 in three top HCI and ML conferences. Based on our fndings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly with respect to how these factors afect women. © 2018 Association for Computing Machinery.

关键词： Paper

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Automatic Prescription Recognition System

Automatic Prescription Recognition System

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IEEE International Conference on Orange Technologies (ICOT)

作者： Yang-Yen Ou Shih-Pang Tseng Jiun Lin Xuan-Ping Zhou Jhing-Fa Wang Ta-Wen Kuan Department of Electrical Engineering National Cheng Kung Pingtung Taiwan Department of Computer Science and Entertainment Technology Tajen University University Pingtung Taiwan General Research Centers of R&D National PingTung University of Science and Technology Pingtung Taiwan

ISBN: (纸本)9781538673201;9781538673195

The study is proposed an automatic prescription recognition system. The system is developed for the visual system of pharmacy robotic technology. Automatic prescription recognition system can help pharmacist to identify medical information in the prescription. We proposed a recognition flow to recognize the information in the prescription. By this system, we can recognize medicines and their usage, routes of administration, dosage, quantity and day. Using the prescription we collected from different hospital, the prescription recognition system reach accuracy of 92.4%.

关键词： Medical diagnostic imaging Insurance Licenses Databases Drugs

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Efficient Emotion Recognition based on Hybrid Emotion Recognition Neural Network

Efficient Emotion Recognition based on Hybrid Emotion Recogn...

引用

IEEE International Conference on Orange Technologies (ICOT)

作者： Yang-Yen Ou Bo-Hao Su Shih-Pang Tseng Liu-Yi-Cheng Hsu Jhing-Fa Wang Ta-Wen Kuan Department of Electrical Engineering National Cheng Kung University Pingtung Taiwan Department of Computer Science and Entertainment Technology Tajen University Pingtung Taiwan General Research Centers of R&D National PingTung University of Science and Technology Pingtung Taiwan

ISBN: (纸本)9781538673201;9781538673195

The practical application of computer vision on robots such as emotion, age, gender recognition can improve the interactive experience between robots and users. This paper uses a webcam to capture the image as a visual system input. Then, facial image is obtained through high-performance face detect neural network. Facial landmarks is used to correct the face. After that, we input facial image into the multi-person emotion recognition system. In order to improve the accuracy of emotion recognition, a hybrid emotion recognition is proposed based on Convolutional Neural Network. Taking facial points and facial image as input, training hybrid neural network to convergence and outputting five home common emotion, neutral, happy, surprise, sad and angry. The other hand, the Microsoft Azure API is used for age and gender recognition. Finally, the experimental result shows that the accuracy of emotion recognition is as high as 86.14%. In practical applications, the system can recognize the emotions, age and gender up to thousands of people at the same time.

关键词： Emotion recognition Neural networks Face Convolution Feature extraction Training Robots

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NMF/NTF-based methods applied for user-guided audio source separation: An overview

NMF/NTF-based methods applied for user-guided audio source s...

引用

2016 International Conference on Orange Technologies, ICOT 2016

作者： Bui, Manh-Quan Duong, Viet-Hang Tseng, Shih-Pang Hong, Zhao-Ze Chen, Bo-Chang Zhong, Zhi-Wei Wang, Jia-Ching Department of Computer Science and Information Engineering National Central University Jhongli Taiwan Department of Computer Science and Entertainment Technology Tajen University Pingtung Taiwan Technology Electronic Systems Research Department National Chung-Shan Institute of Science Taiwan Stone and Resources Industry RandD Center Taiwan

ISBN: (纸本)9781538648315

Recently, user-guided or user-assisted audio separation, which exploits the auxiliary information of sources provided by users, is a new direction in audio source separation. Nonnegative matrix or tensor factorization (NMF/NTF) is one of the promising classes of approaches to address this task. In this paper, we present a survey of recent developments in user-guided source separation approaches based on NMF/NTF and illustrate various ways for incorporating the provided knowledge from user. © 2016 IEEE.

关键词： Tensors

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A memetic gravitation search algorithm for solving clustering problems

A memetic gravitation search algorithm for solving clusterin...

引用

Congress on Evolutionary Computation

作者： Ko-Wei Huang Jui-Le Chen Chu-Sing Yang Chun-Wei Tsai Department of Electrical Engineering National Cheng Kung University Tainan Taiwan ROC Department of Computer Science and Entertainment Technology Tajen university Pingtung Taiwan ROC Department of Computer Science and Information Engineering National Ilan University Yiian Taiwan ROC

ISBN: (纸本)9781479974931

The clustering problem is among the most important optimization problems. Given that it is an NP-hard problem, it can be efficiently solved using meta-heuristic algorithms such as the gravitation search algorithm (GSA). GSA is a new swarm-based algorithm particularly suitable for solving NP-hard combinatorial optimization problems. This paper solves the clustering problem with a newly proposed memetic GSA (MGSA) algorithm. MGSA is coupled with the pattern reduction operator and the multi-start operator. The proposed MGSA algorithm was verified on six UCI benchmarks and images segmentation. Based on a performance comparison amongst MGSA, the original GSA, and two state-of-the-art meta-heuristic algorithms (Firefly algorithm and the Artificial bee colony algorithm), we observe that the proposed algorithm can significantly reduce computation time without compromising much on the quality of the solution.

关键词： Clustering algorithms Partitioning algorithms Accuracy Memetics Benchmark testing Sociology Statistics

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Gesture recognition based on Kinect

Gesture recognition based on Kinect

引用

Advanced Technologies, Embedded and Multimedia for Human-Centric Computing, HumanCom and EMC 2013

作者： Chuang, Chi-Hung Chen, Ying-Nong Deng, Ming-Sang Fan, Kuo-Chin Department of Computer Engineering and Entertainment Technology Tajen University Pingtung Taiwan

ISBN: (纸本)9789400772618

In recent years, depth cameras have become a widely available sensor type that captures depth images at real-time frame rates. For example, Microsoft KINECT is a powerful but cheap device to get depth images. Even though recent approaches have shown that 3D pose estimation and recognition from monocular 2.5D depth images has become feasible, there are still some challenge problems like gesture detection and recognition. In this paper, we propose a gesture recognition method and use that to make a puzzle game with kinesthetic system. Gesture is very important for our system because instead of using some devices like keyboard or mouse, users will play the puzzle game by their own hand. We will focus on using ROI and some algorithms that we proposed to do gesture detection and recognition. © Springer Science+Business Media Dordrecht 2014.

关键词： Gesture recognition

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