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Hierarchical Feature Warping and Blending for Talking Head Animation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第8期34卷 7301-7314页

作者： Zhang, Jiale Liu, Chengxin Xian, Ke Cao, Zhiguo Huazhong Univ Sci & Technol Minist Educ Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Talking head animation transforms a source anime image to a target pose, where the transformation includes the change of facial expression and head movement. In contrast to existing approaches that operate on the low-resolution image (256 x 256), we study this task at a higher resolution, e.g., 512 x 512 . High-resolution talking head animation, however, raises two major challenges: i) how to achieve smooth global transformation while maintaining rich details of anime characters under large-displacement pose variations;ii) how to address the shortage of data, because no related dataset is publicly available. In this paper, we present a Hierarchical Feature Warping and Blending (HFWB) model, which tackles talking head animation hierarchically. Specifically, we use low-level features to control global transformation and high-level features to determine the details of anime characters, under the guidance of feature flow fields. These features are then blended by selective fusion units, outputting transformed anime images. In addition, we construct an anime pose dataset-AniTalk-2K, aiming to alleviate the shortage of data. It contains around 2000 anime characters with thousands of different face/head poses at a resolution of 512 x 512 . Extensive experiments on AniTalk-2K demonstrate the superiority of our approach in generating high-quality anime talking heads over state-of-the-art methods.

关键词： Head image resolution Task analysis Generators Vectors Animation Iterative methods Talking head animation generative adversarial networks pose transformation anime image generation anime dataset

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Fusion algorithm for multisensor images based on discrete multiwavelet transform

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IEE PROCEEDINGS-VISION image AND SIGNAL processing 2002年第5期149卷 283-289页

作者： Wang, H Peng, J Wu, W Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The authors review the recent notion of multiwavelets and describe the use of the discrete multiwavelet transform (DMWT) in image fusion processing. Multiwavelets are extensions from scalar wavelets, and have several advantages in comparison with scalar wavelets. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. A novel fusion algorithm is presented for multisensor images based on the discrete multiwavelet transform that can be performed at pixel level. After the registering of source images, a pyramid for each source image can be obtained by applying decomposition with multiwavelets in each level. The multiwavelet decomposition coefficients of the input images are appropriately merged and a new fused image is obtained by reconstructing the fused multiwavelet coefficients. This image fusion algorithm may be used to combine images from multisensors to obtain a single composite with extended information content. The results of experiments indicate that this image fusion algorithm can provide a more satisfactory fusion outcome.

关键词： .DWT source images image fusion Multiwavelets Multisensor image

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Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第1期32卷 015004-015004页

作者： Zhang, Changhe Kong, Li Xu, Qi Zhou, Kaibo Pan, Hao Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Rotating machinery is a primary element of mechanical equipment, and thus fault diagnosis of its key components is very important to improve the reliability and safety of modern industrial systems. The key point to diagnose the faults of these components is to extract effectively the hidden fault information. However, the actual vibration signals of rotating machinery have nonlinear and non-stationary characteristics, so traditional signal decomposition methods are unable to extract the frequency components accurately, leading to spectrum overlap of the decomposed sub-signals. Therefore, a rotating machinery fault diagnosis approach based on Fourier transform multi-filter decomposition (FTMFD), fuzzy entropy (FE), joint mutual information maximization (JMIM), and a light gradient boosting machine (LightGBM), is proposed in this paper. FTMFD is used to extract the frequency domain information of the raw vibration signals, whereas FE is used to calculate and extract the fault information of the decomposed sub-signals. Then feature selection is carried out by using JMIM to reduce the influence of redundant features on data analysis and classification accuracy. Furthermore, LightGBM is used to rank the candidate features and outputs the fault diagnosis result. Experimental results from two real datasets show that the proposed method achieves higher accuracy with fewer features than some existing methods for fault recognition. Various working conditions are also considered and verified.

关键词： Fourier transform multifilter decomposition fuzzy entropy joint mutual information maximization LightGBM classifier rotating machinery fault diagnosis

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Complementation-Reinforced Attention Network for Person Re-Identification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第10期30卷 3433-3445页

作者： Han, Chuchu Zheng, Ruochen Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Fine-grained information has been proved helpful for person re-identification, and multi-head attention mechanism offers a feasible solution for this. However, we observe severe redundancy among the multiple branches, which might make the learned representation over-emphasize certain discriminative regions and correspondingly ignore other potentially informative regions. Therefore, we tackle this issue by two aspects yielding the so-called Complementation-Reinforced Attention Network (CRAN). One is the redundancy among branches, and we propose to impose complementing constraints among multiple attention heads. The constraints are two-fold: on the one hand, it encourages each branch to attend to complementary attention regions;on the other hand, it enforces orthogonality among the learned features of different regions in the embedding space. The other is the redundancy among query positions for each attention head. So we simplify the attention block by sparsifying the query positions. Besides, in order to achieve efficient retrieval, we propose an adaptive feature fusion method for dimensional reduction. Compared with the commonly used feature ensemble, our method effectively reduces the dimensionality while keeping the discriminative ability. We demonstrate the effectiveness of our method on MSMT17, Market-1501, DukeMTMC-reID, and CUHK03 datasets.

关键词： Task analysis Redundancy Feature extraction Head Visualization Optimization Measurement Person re-identification attention complementation

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Optimal Spatial-Temporal Scheduling for EVs With Stochastic Behaviors and Possible Inauthentic Information

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IEEE TRANSACTIONS ON SMART GRID 2024年第1期15卷 729-742页

作者： Wang, Yan-Wu Cao, Zhong Xiao, Jiang-Wen Liu, Xiao-Kang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper studies the spatial-temporal scheduling for electric vehicles with stochastic charging behaviors. A new spatial-temporal scheduling model is proposed to achieve both the spatial coordination and temporal coordination for EV users and is modeled as a non-convex optimization problem which can be solved by the coordinate descent method. Further, a new billing method of charging cost is proposed based on the Vickrey-Clarke-Groves to avoid possible inauthentic information from users. Specially, the individual rationality and incentive compatibility of the method are theoretically proved. Moreover, a stochastic optimization problem is proposed to describe the stochastic charging behavior of users, which can fit the real application better than the deterministic one. Consequently, an efficient charging strategy is obtained via chance constraint method. Simulation results and comparison studies show the effectiveness of both the model and the billing method.

关键词： Electric vehicle charging strategy chance constraint inauthentic information coordinate descent method

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Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2020年第5期28卷 1117-1127页

作者： Zhang, Wen Wu, Dongrui Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks. This paper considers offline unsupervised cross-subject electroencephalogram (EEG) classification, i.e., we have labeled EEG trials from one or more source subjects, but only unlabeled EEG trials from the target subject. We propose a novel manifold embedded knowledge transfer (MEKT) approach, which first aligns the covariance matrices of the EEG trials in the Riemannian manifold, extracts features in the tangent space, and then performs domain adaptation by minimizing the joint probability distribution shift between the source and the target domains, while preserving their geometric structures. MEKT can cope with one or multiple source domains, and can be computed efficiently. We also propose a domain transferability estimation (DTE) approach to identify the most beneficial source domains, in case there are a large number of source domains. Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.

关键词： Covariance matrices Electroencephalography Manifolds Feature extraction Task analysis Geometry Symmetric matrices Brain-computer interfaces electroencephalogram Riemannian manifold transfer learning

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Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent With Regularization, DropRule, and AdaBound (MBGD-RDA)

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第5期28卷 1003-1015页

作者： Wu, Dongrui Yuan, Ye Tan, Yihua Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China

Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance and also enables them to deal with big data. Inspired by the connections between TSK fuzzy systems and neural networks, we extend three powerful neural network optimization techniques, i.e., minibatch gradient descent (MBGD), regularization, and AdaBound, to TSK fuzzy systems, and also propose three novel techniques (DropRule, DropMF, and DropMembership) specifically for training TSK fuzzy systems. Our final algorithm, MBGD with regularization, DropRule, and AdaBound, can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing. It can be used for training TSK fuzzy systems on datasets of any size;however, it is particularly useful for big datasets, on which currently no other efficient training algorithms exist.

关键词： AdaBound Adaptive-network-based fuzzy inference system (ANFIS) DropRule fuzzy systems minibatch gradient descent (MBGD) regularization

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Pseudo label Fusion With Uncertainty Estimation for Semi-Supervised Cropping Box Regression

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 8157-8171页

作者： Pan, Zhiyu Cui, Jiahao Wang, Kewei Wu, Yizheng Cao, Zhiguo Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China

Cropping box regression algorithms re-frame the images with predicted cropping boxes for better composition quality, which can save considerable manpower and time for massive image retouching work. Yet, recent learning-based cropping box regression algorithms require expert annotations, which makes the scale of training limited. This consequently incurs a performance bottleneck. To address this issue, previous works seek the help from auxiliary datasets of related tasks, e.g., the composition classification. However, the domain gap between related tasks and the likewise restricted scale of auxiliary datasets are still limiting factors. Hence, our work provides a novel semi-supervised framework that can learn better re-framing knowledge with unlimited unlabeled data. We make use of the unlabeled data via pseudo-labeling, where the model learns from the pseudo labels generated from a temporal ensemble version of itself. To prevent the model learns from its own mistakes, a.k.a. the problem of confirmation bias, we propose to rectify the mistakes by fusing multiple candidate pseudo labels into the better ones. The fusion procedure is based on the uncertainty estimation for each boundary of the candidate cropping boxes. The multiple candidates are from the proposed aesthetic region proposal network. Extensive experimental results explain how the uncertainty-based pseudo label fusion procedure overcomes the confirmation bias and demonstrate the superiority of our semi-supervised cropping box regression framework.

关键词： Task analysis Uncertainty Annotations Semisupervised learning Object detection Data models Multitasking image cropping cropping box regression semi-supervised learning uncertainty estimation

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Bandpass Effect and Its Compensation Method for Diagnosis in a Closed-Loop control System

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2020年第3期25卷 1679-1689页

作者： Yao, Yuan Li, Yesong Yin, Quan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China

Motor current signal analysis is an effective technique for fault diagnosis in the motor drive system. However, in the closed-loop control (CLC) system, the amplitude of periodic faulty harmonic is suppressed by the impact of CLC characteristics, so the severity assessment of faults may fail. In this article, a model reference method based on the theoretical analysis of CLC characteristics is proposed to deal with this problem. First, the faulty harmonic in the q-axis current is extracted from the field-oriented control process as a fault indicator. Then, the bandpass effect is revealed to describe the variation of amplitudes of harmonics in the current spectrum. The variation is caused by the control parameter and architecture in the CLC system. Finally, to improve diagnostic performance, a model reference method is proposed to compensate for the aforementioned variation. The model is established by experimentally measuring the amplitude-frequency response of the closed-loop speed control system. The bandpass effect of the CLC system is validated by simulations in the MATlab/Simulink environment. Furthermore, experimental validations are conducted on a ball screw platform. The results show that the proposed method is effective to compensate the bandpass effect and helps to achieve better diagnostic performance.

关键词： Bandpass effect closed-loop control (CLC) system fault diagnosis model reference method motor current signal analysis (MCSA)

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The sensitivity analysis of full digital compensatory MMW radiometer

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INTERNATIONAL JOURNAL OF INFRARED AND MILLIMETER WAVES 2007年第7期28卷 579-585页

作者： Gui, LiangQi Chen, Ke Lang, Liang Guo, Wei Huazhong Univ Sci & Technol Dept Elect & Informat Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

The digital compensatory millimeter wave radiometer is a new type of radiometer, which uses the digital compensatory operator to compensate gain and receiver noise fluctuation. This paper aims at the full digital compensatory 8 MMW radiometer which was designed, proposing the analysis method of its sensitivity, obtaining the mathematical formulae of its equivalent integration time and the best sensitivity adjustment method by software.

关键词： MMW radiometer sensitivity integration time digital integrator

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