A survey of the vision transformers and their CNN-transformer based variants
Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture
Histopathological image recognition of breast cancer is an onerous task. Although many
deep learning models have achieved good classification results on histopathological image …
deep learning models have achieved good classification results on histopathological image …
Few-shot learning for facial expression recognition: a comprehensive survey
Facial expression recognition (FER) is utilized in various fields that analyze facial
expressions. FER is attracting increasing attention for its role in improving the convenience …
expressions. FER is attracting increasing attention for its role in improving the convenience …
Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals
Depression stands as one of the most widespread psychological disorders and has
garnered increasing attention. Currently, how to effectively achieve automatic multimodal …
garnered increasing attention. Currently, how to effectively achieve automatic multimodal …
[HTML][HTML] A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest
X Lu, R Yang, J Zhou, J Jiao, F Liu, Y Liu, B Su… - Journal of King Saud …, 2022 - Elsevier
Disease and pest are the main factors causing grape yield reduction. Correct and timely
identification of these symptoms are necessary for the vineyard. However, the commonly …
identification of these symptoms are necessary for the vineyard. However, the commonly …
A deeper look into aleatoric and epistemic uncertainty disentanglement
Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue.
Uncertainty quantification is required for many applications, and disentangled aleatoric and …
Uncertainty quantification is required for many applications, and disentangled aleatoric and …
An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices
For several years the modeling as well as forecasting of the prices of stocks have been
extremely challenging for the business community and researchers as a result of the …
extremely challenging for the business community and researchers as a result of the …
Self-supervised vision transformer-based few-shot learning for facial expression recognition
X Chen, X Zheng, K Sun, W Liu, Y Zhang - Information Sciences, 2023 - Elsevier
Facial expression recognition (FER) is embedded in many real-world human-computer
interaction tasks, such as online learning, depression recognition and remote diagnosis …
interaction tasks, such as online learning, depression recognition and remote diagnosis …
Hierarchical attention network with progressive feature fusion for facial expression recognition
H Tao, Q Duan - Neural Networks, 2024 - Elsevier
Facial expression recognition (FER) in the wild is challenging due to the disturbing factors
including pose variation, occlusions, and illumination variation. The attention mechanism …
including pose variation, occlusions, and illumination variation. The attention mechanism …
MMATrans: Muscle movement aware representation learning for facial expression recognition via transformers
How to automatically recognize facial expression has caused concerns in industrial human–
robot interaction. However, facial expression recognition (FER) is susceptible to problems …
robot interaction. However, facial expression recognition (FER) is susceptible to problems …