Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Sun, Qigong"'
Toward Motion Robustness: A masked attention regularization framework in remote photoplethysmography
There has been growing interest in facial video-based remote photoplethysmography (rPPG) measurement recently, with a focus on assessing various vital signs such as heart rate and heart rate variability. Despite previous efforts on static datasets, t
Externí odkaz:
http://arxiv.org/abs/2407.06653
Autor:
Sun, Qigong, Li, Xiufang, Shang, Fanhua, Liu, Hongying, Yang, Kang, Jiao, Licheng, Lin, Zhouchen
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage. Thus, DNNs cannot be efficiently applied to mobile phones and embedded devices, which severely limits their applicability in indust
Externí odkaz:
http://arxiv.org/abs/2106.09886
As an effective technique to achieve the implementation of deep neural networks in edge devices, model quantization has been successfully applied in many practical applications. No matter the methods of quantization aware training (QAT) or post-train
Externí odkaz:
http://arxiv.org/abs/2105.01353
Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardware (e.g., mobile phones and embedded devices). The existing quantizat
Externí odkaz:
http://arxiv.org/abs/2103.05363
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart chips. The mixed-precision quantization model can match different quan
Externí odkaz:
http://arxiv.org/abs/2103.02904
Autor:
Wang, Dong, Liu, Yicheng, Tang, Wenwo, Shang, Fanhua, Liu, Hongying, Sun, Qigong, Jiao, Licheng
In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks. We first introduce the sign operation of stochastic gradients (as in sign-based methods, e.g., SIGN-SGD) into ADAM, which is called as signADAM. Moreo
Externí odkaz:
http://arxiv.org/abs/1907.09008
Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challenges, e.g., deficiency of labeled data, inadequate utilization of data i
Externí odkaz:
http://arxiv.org/abs/1906.03605
Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs). In this paper, based on the mechanism of the population receptive field (pRF) in human visual cortex, we further utilize
Externí odkaz:
http://arxiv.org/abs/1906.03607
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance. Thus, DNNs cannot be efficiently applied to mobile phones and embedded devices, which seriously limits their applicability in i
Externí odkaz:
http://arxiv.org/abs/1905.13389
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 5, pp. 3040-3054, May 2019
The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix ob
Externí odkaz:
http://arxiv.org/abs/1807.02975