How to represent a human face pattern?While it is presented in a continuous way in human visual system,computers often store and process it in a discrete manner with 2D arrays of *** authors attempt to learn a continu...
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How to represent a human face pattern?While it is presented in a continuous way in human visual system,computers often store and process it in a discrete manner with 2D arrays of *** authors attempt to learn a continuous surface representation for face image with explicit ***,an explicit model(EmFace)for human face representation is pro-posed in the form of a finite sum of mathematical terms,where each term is an analytic function ***,to estimate the unknown parameters of EmFace,a novel neuralnetwork,EmNet,is designed with an encoder-decoder structure and trained from massive face images,where the encoder is defined by a deep convolutional neuralnetwork and the decoder is an explicit mathematical expression of *** authors demonstrate that our EmFace represents face image more accurate than the comparison method,with an average mean square error of 0.000888,0.000936,0.000953 on LFW,IARPA Janus Benchmark-B,and IJB-C *** results show that,EmFace has a higher representation performance on faces with various expressions,postures,and other ***,EmFace achieves reasonable performance on several face image processing tasks,including face image restoration,denoising,and transformation.
In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic ***,as"black box"models,they have limited theoretical *** this work,we...
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In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic ***,as"black box"models,they have limited theoretical *** this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to *** demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.
Implicit polynomial(IP)fitting is an effective method to quickly represent two-dimensional(2D)image boundary contour in the form of mathematical *** the same maximum degree,the fractional implicit polynomial(FIP)can e...
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Implicit polynomial(IP)fitting is an effective method to quickly represent two-dimensional(2D)image boundary contour in the form of mathematical *** the same maximum degree,the fractional implicit polynomial(FIP)can express more curve details than IP and has obvious advantages for the representation of complex boundary *** existing studies,algebraic distance is mainly used as the fitting objective of the *** the time cost is reduced,there are problems of low fitting accuracy and spurious zero *** this paper,we propose a two-stage neuralnetwork with differentiable geometric distance,which uses FIP to achieve mathematical representation,called *** the first stage,the continuity constraint is used to obtain a rough outline of the fitting *** the second stage,differentiable geometric distance is gradually added to fine-tune the polynomial coefficients to obtain a contour representation with higher *** results show that TSEncoder can achieve mathematical representation of 2D image boundary contour with high accuracy.
Image quality assessment is to simulate subjective human visual perception and realize image quality inference automatically. Although deep neuralnetworks have achieved great success, the majority of them do not full...
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Convolutional neuralnetwork (CNN) is an artificial intelligence algorithm with a wide application foundation. In recent years, to improve the computational speed of the computation-intensive CNN models, many research...
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This paper proposes a generic neuralnetwork fitting algorithm based on CNN for nonlinear functions that overcomes the challenges of a large number of nonlinear functions in terms of hardware deployment and computing ...
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Deep convolutional neuralnetworks (DCNs) have recently experienced rapid development in the direction of lightweight and edge deployment. However, accelerators for DCNs face challenges in balancing computational and ...
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Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronav...
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The development of lightweight networks makes neuralnetworks more efficient to be widely applied to various tasks. Considering the deployment of hardware like edge devices and mobile phones, we prioritize lightweight...
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Large Language Models (LLMs) have made incredible strides recently in understanding and reacting to user intents. However, these models typically excel in English and have not been specifically trained for medical app...
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