Hui Yu

University of Portsmouth

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i-10 index

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Junyu Dong

Ocean University of China

Muwei Jian

Shandong University of Finance and Economics

Honghai Liu

Harbin Institute of Technology

Lin Qi

Ocean University of China

Shu Zhang

Sichuan University

Fei‐Yue Wang

Chinese Academy of Sciences

Hanwen Luo

Fourth Affiliated Hospital of Guangxi Medical University

Rung-Ching Chen

Chaoyang University of Technology

Xiaoqiang Yan

Zhengzhou University

Gongfa Li

Wuhan University of Science and Technology

Cooperative Institutions

Chinese Academy of Sciences

201

Shanghai Jiao Tong University

121

Institute of Electrical and Electronics Engineers

115

Ocean University of China

University of Chinese Academy of Sciences

Shandong University

Sichuan University

Tsinghua University

Huazhong University of Science and Technology

Gorgias Press (United States)

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A review on the attention mechanism of deep learning

Neurocomputing (2021)

Zhaoyang Niu Guoqiang Zhong Hui Yu

Interpretability

Representation

Feature (linguistics)

Deep Neural Networks

10.1016/j.neucom.2021.03.091

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Citations (1,872)

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Journal of Ambient Intelligence and Humanized Computing (2021)

Christine Dewi Rung-Ching Chen Hui Yu Xiaoyi Jiang

Pooling

Pyramid (geometry)

Traffic Sign Recognition

10.1007/s12652-021-03584-0

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Citations (57)

Photogenerated Carbohydrate Microarrays

ChemBioChem (2006)

Zhichao Pei Hui Yu Matthias Theurer Annelie Waldén Peter Nilsson

Sugars in a row. A new strategy for carbohydrate microarrays based on photochemical ligation of perfluorophenylazide-derivatized carbohydrates to PEO surfaces is presented. It constitutes a controllable and robust method of array fabrication, on the carbohydrate-chemistry and on the surface-chemistry levels, and the resulting carbohydrate arrays can be efficiently used to reveal the recognition patterns of carbohydrate-binding proteins. Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2268/2007/z600447_s.pdf or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Carbohydrate chemistry

Carbohydrate-binding module

10.1002/cbic.200600447

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Citations (61)

Unsupervised Video Summarization Based on the Diffusion Model of Feature Fusion

IEEE Transactions on Computational Social Systems (2024)

Qinghao Yu Hui Yu Ying Sun Derui Ding Muwei Jian

Video summarization (VS) technologies can automatically extract key frames with effective information and thus can help to quickly identify the events or speed up the decision-making process, especially for accidents. With the fast development of deep learning technologies, many generative adversarial network (GAN)- and reinforcement learning (RL)-based unsupervised VS methods have been developed in recent years. However, these methods could suffer from the problems of unstable training and difficulty of reward function formulation, respectively. To this end, we present an unsupervised VS method called diffusion model of feature fusion (DMFF) in this article, which consists of a diffusion module (DM), a feature extraction and compression module (FECM), and a coarse-fine frame selector (CFFS). DM is designed to avoid the training instability problem caused by GAN's alternate training generator and discriminator. FECM is used to extract and compress video features. CFFS is designed to capture both low-level and high-level features between frames to handle complex and diverse accident videos. Then, high-level local and global features are fused to generate a multigrained final frame score. Experiments on two widely used benchmark datasets, SumMe and TVSum, demonstrate the effectiveness and superiority of the proposed network to the state-of-the-art methods, and the training is more stable.

Benchmark (surveying)

Discriminator

Feature (linguistics)

Key frame

Fuse (electrical)

10.1109/tcss.2024.3384627

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Citations (0)

Phenylacetic acid-modified nanofibrous polystyrene membranes for use as highly sensitive ammonia sensors

Sensors and Actuators B Chemical (2015)

Yongtang Jia Hui Yu Yumei Zhang Lizhu Chen Fengchun Dong

Polystyrene

Phenylacetic acid

10.1016/j.snb.2015.02.041

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Citations (15)

Q-learning Based Meta-heuristics for Solving Distribute Permutation Flowshop Problems

Hanxiang Zheng Kaizhou Gao Bohan Qiu Hui Yu Weiyu Tang

Heuristics

10.23919/ccc63176.2024.10662545

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Citations (0)

Distilling Ordinal Relation and Dark Knowledge for Facial Age Estimation

IEEE Transactions on Neural Networks and Learning Systems (2020)

Qilu Zhao Junyu Dong Hui Yu Sheng Chen

In this article, we propose a knowledge distillation approach with two teachers for facial age estimation. Due to the nonstationary patterns of the facial-aging process, the relative order of age labels provides more reliable information than exact age values for facial age estimation. Thus, the first teacher is a novel ranking method capturing the ordinal relation among age labels. Especially, it formulates the ordinal relation learning as a task of recovering the original ordered sequences from shuffled ones. The second teacher adopts the same model as the student that treats facial age estimation as a multiclass classification task. The proposed method leverages the intermediate representations learned by the first teacher and the softened outputs of the second teacher as supervisory signals to improve the training procedure and final performance of the compact student for facial age estimation. Hence, the proposed knowledge distillation approach is capable of distilling the ordinal knowledge from the ranking model and the dark knowledge from the multiclass classification model into a compact student, which facilitates the implementation of facial age estimation on platforms with limited memory and computation resources, such as mobile and embedded devices. Extensive experiments involving several famous data sets for age estimation have demonstrated the superior performance of our proposed method over several existing state-of-the-art methods.

10.1109/tnnls.2020.3009523

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Citations (26)

Local and Global Perception Generative Adversarial Network for Facial Expression Synthesis

IEEE Transactions on Circuits and Systems for Video Technology (2021)

Yifan Xia Wenbo Zheng Yiming Wang Hui Yu Junyu Dong

Facial expression synthesis has gained increasing attention with the development of Generative Adversarial Networks (GANs). However, it is still very challenging to generate high-quality facial expressions since the overlapping and blur commonly appear in the generated facial images especially in the regions with rich facial features such as eye and mouth. Generally, existing methods mainly consider the face as a whole in facial expression synthesis without paying specific attention to the characteristics of facial expressions. In fact, according to the physiological and psychological research, the differences of facial expressions often appear in crucial regions such as eye and mouth. Motivated by this observation, a novel end-to-end facial expression synthesis method called Local and Global Perception Generative Adversarial Network (LGP-GAN) with a two-stage cascaded structure is proposed in this paper which is designed to extract and synthesize the details of the crucial facial regions. LGP-GAN can combine the generated results from the global network and local network into the corresponding facial expressions. In Stage I, LGP-GAN utilizes local networks to capture the local texture details of the crucial facial regions and generate local facial regions, which fully explores crucial facial region domain information in facial expressions. And then LGP-GAN uses a global network to learn the whole facial information in Stage II to generate the generate final facial expressions building upon local generated results from Stage I. We conduct qualitative and quantitative experiments on the commonly used public database to verify the effectiveness of the proposed method. Experimental results show the superiority of the proposed method over the state-of-the-art methods.

Generative adversarial network

Face hallucination

Expression (computer science)

10.1109/tcsvt.2021.3074032

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Citations (48)

Robust and Fast Spatial Verification

Applied Mechanics and Materials (2011)

Wen Ying Wang Ru Guo Hui Yu Si Tian

Spatial verification for object retrieval is often time-consuming and susceptible to viewpoint changes. In this paper, we propose a novel spatial verification method that is robust to viewpoint changes. Firstly, the affine covariant neighborhoods (ACNs) of corresponding local regions are matched to eliminate possible false matches. Secondly, the RANSAC is performed to estimate the affine transformation from each single pair of corresponding local regions without the gravity vector assumption used in previous spatial verification methods. Experimental results demonstrate that this method is more robust and fast than previous spatial verification methods.

RANSAC

10.4028/www.scientific.net/amm.109.466

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Citations (0)

Ultrasonic cavitation strengthening and generation of superhydrophobicity for the surface of in situ (ZrB2 + Al2O3)/AA6016 matrix composite

Surface and Coatings Technology (2024)

Haixia Liu Yongyuan Chen Jie Chen Hui Yu

Matrix (chemical analysis)

10.1016/j.surfcoat.2024.130878

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Citations (1)