In the pre-training phase, we use a fully convolutional-based masked autoencoder (FCMAE) to reconstruct full images from partially masked inputs. The encoder of FCMAE aggregates contextual information to infer the masked image regions, and this pretrained encoder is then migrated to the ...
This is an official implementation of Auto-AD in our TGRS 2021 paper " Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder ". - RSIDEA-WHU2020/Auto-AD
we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents pr...
(default parameter for masked language models) of the tokens to create a self-supervised training task. In this training task, polyBERT is taught to predict the masked tokens using the non-masked surrounding tokens by adjusting the weights of the Transformer encoders (fill-in-the-blanks task)...
This study introduces an effective approach using a fully convolutional mesh autoencoder model to reconstruct 3D facial features in the presence of imperfections. The method accurately simulates facial scars in a virtual environment, adapting to unique situations. This article presents the "Cir3D-FaIR...
Fully Convolutional Siamese Autoencoder for Change Detection in UAV Aerial Imagesdoi:10.1109/lgrs.2019.2945906Daniel B. MesquitaRonaldo F. dos SantosDouglas G. MacharetMario F. M. CamposErickson R. NascimentoIEEE
Subsequently, based on the distinct data types, targeted one-dimensional fully convolutional autoencoder models are constructed to effectively achieve dimensionality reduction compression and reconstruction of the MFL data. Through practical experimental analysis, the reconstruction error such as MAE is ...
The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not contain a fully-connected layer is employed for the encoder-...
In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture defects based on a small number of defect-free texture samples. The proposed MS-FCAE method utilizes...
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoderdoi:10.1016/J.CVIU.2020.102920Yaxiang FanGongjian WenDeren LiShaohua QiuMartin D. LevineFei XiaoAcademic Press