Search Results - "Masked autoencoder"

A robust operators’ cognitive workload recognition method based on denoising masked autoencoder by Yu, Xiaoqing, Chen, Chun-Hsien

“…Identifying the cognitive workload of operators is crucial in complex human-automation collaboration systems. An excessive workload can lead to fatigue or…”
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Rethinking link prediction: A multi-scale graph masked autoencoder by Zhang, Guotai, Zuo, Enguang, Yan, Ziwei, Chen, Chen, Chen, Cheng, Lv, Xiaoyi

“…In link prediction tasks, Graph Self-Supervised Learning (GSSL) has enormous potential for tackling the fundamental issue of sparse labels in real-world graph…”
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MAEDAY: MAE for few- and zero-shot AnomalY-Detection by Schwartz, Eli, Arbelle, Assaf, Karlinsky, Leonid, Harary, Sivan, Scheidegger, Florian, Doveh, Sivan, Giryes, Raja

“…We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous…”
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Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing by Wang, Yi, Hernandez, Hugo Hernandez, Albrecht, Conrad M, Zhu, Xiao Xiang

“…Self-supervised learning guided by masked image modeling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in…”
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Cross-modality masked autoencoder for infrared and visible image fusion by Bi, Cong, Qian, Wenhua, Shao, Qiuhan, Cao, Jinde, Wang, Xue, Yan, Kaixiang

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SS-MAE: Spatial-Spectral Masked Autoencoder for Multisource Remote Sensing Image Classification by Lin, Junyan, Gao, Feng, Shi, Xiaochen, Dong, Junyu, Du, Qian

“…Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. The existing MIM-based methods mostly…”
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Point-GSMAE: A graph convolution and scale-based masked autoencoder for 3D point cloud representation by Bai, Yun, Yang, Chaozhi, Li, Guanlin, He, Xiao, Xiao, Qian, Li, Zongmin

“…Masked Autoencoders (MAEs) have demonstrated considerable potential in advancing self-supervised learning for 3D point cloud representation. Nevertheless,…”
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Reversible data hiding in encrypted images based on pixel-level masked autoencoder and polar code by Cheng, Zhangpei, Chen, Kaimeng, Guan, Qingxiao

“…•A novel pixel-level mask autoencoder (PLMAE) is proposed to build a high-performance image recovery mechanism.•The idea of channel coding is used in the data…”
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MASA: Motion-Aware Masked Autoencoder With Semantic Alignment for Sign Language Recognition by Zhao, Weichao, Hu, Hezhen, Zhou, Wengang, Mao, Yunyao, Wang, Min, Li, Houqiang

“…Sign language recognition (SLR) has long been plagued by insufficient model representation capabilities. Although current pre-training approaches have…”
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HiCMAE: Hierarchical Contrastive Masked Autoencoder for self-supervised Audio-Visual Emotion Recognition by Sun, Licai, Lian, Zheng, Liu, Bin, Tao, Jianhua

“…Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in recent years for its critical role in creating emotion-aware intelligent machines…”
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MAE-EEG-Transformer: A transformer-based approach combining masked autoencoder and cross-individual data augmentation pre-training for EEG classification by Cai, Miao, Zeng, Yu

“…Convolutional neural networks (CNN) may not be ideal for extracting global temporal features from non-stationary Electroencephalogram (EEG) signals. The…”
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Transformer-Based Masked Autoencoder With Contrastive Loss for Hyperspectral Image Classification by Cao, Xianghai, Lin, Haifeng, Guo, Shuaixu, Xiong, Tao, Jiao, Licheng

“…In recent years, in order to solve the problem of lacking accurately labeled hyperspectral image data, self-supervised learning has become an effective method…”
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DeepVATS: Deep Visual Analytics for Time Series by Rodriguez-Fernandez, Victor, Montalvo-Garcia, David, Piccialli, Francesco, Nalepa, Grzegorz J., Camacho, David

“…The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to…”
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TrafficMAE: A network traffic classification model based on masked autoencoder by Zhu, Wenjian, Zhang, Haiming, Pei, Changhua, Li, Jian

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Enhanced few-shot state-of-health estimation for lithium-ion batteries via Masked Autoencoder by Shen, Yifan, Guo, Dongxu, Wang, Yu, Chen, Jianguo, Liu, Xuyang, Han, Xuebing, Zheng, Yuejiu, Ouyang, Minggao

“…Accurately estimating the state-of-health (SOH) of lithium-ion batteries (LIBs) is crucial for optimizing performance, ensuring operational safety, and…”
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MAEMACD: a MAE-enhanced multiresolution attention network for remote sensing image change detection by Su, Linzhi, Chen, Yuji, Qu, Yang, Zhu, Zijie, Cao, Xin

“…The task of remote sensing image change detection aims to identify significant changes between two images, which is crucial for understanding terrestrial…”
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MRFuse: Metric learning and masked autoencoder for fusing real infrared and visible images by Li, YuBin, Zhan, Weida, Guo, Jinxin, Zhu, Depeng, Jiang, Yichun, Chen, Yu, Xu, Xiaoyu, Han, Deng

“…The task of infrared and visible image fusion aims to retain the thermal targets from infrared images while preserving the details, brightness, and other…”
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SDMAE: A self-supervised learning method based on a self-distillation masked autoencoder for anomalous sound detection by Ye, Shengheng, Yu, Junhe

“…•A self-supervised pre-training approach is proposed to detect unknown anomalous sounds, enhancing the model’s generalization ability.•The unique…”
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Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing by Yu, Zitong, Cai, Rizhao, Cui, Yawen, Liu, Xin, Hu, Yongjian, Kot, Alex C.

“…Recently, vision transformer (ViT) based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However,…”
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Dual-stage learning framework for underwater acoustic target recognition with cross-attention mechanism and audio-guided contrastive learning by Zhao, Rongyao, Liu, Feng, Zhao, Lyufang, Li, Daihui, Xu, Jing, Liu, Yuanxin, Shen, Tongsheng

“…Underwater acoustic target recognition is crucial for marine exploration and environmental monitoring. However, the redundancy in raw time-domain signals and…”
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