Vision Transformers in Image Restoration: A Survey by Anas M. Ali, Bilel Benjdira, Anis Koubaa, Walid El-Shafai, Zahid Khan, Wadii Boulila

“…This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. …”
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GSAtt-CMNetV3: Pepper Leaf Disease Classification Using Osprey Optimization by Shaik Salma Asiya Begum, Hussain Syed

“…Hence, this study proposes a novel optimized DL model for classifying the presence and absence of pepper leaf disease using an effective feature learning process. The proposed study undergoes four major stages namely Pre-processing, Segmentation, Feature extraction, and Classification. …”
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Fault Diagnosis Method for Rotating Machinery Based on Multi-scale Features by Ruijun Liang, Wenfeng Ran, Yao Chen, Rupeng Zhu

“…On the widely used Case Western Reserve University Bearing Dataset, this paper conducted a performance comparison between the proposed MBCNN and other baselines including the shallow learning methods, 1D-CNN, and multi-scale feature learning methods. Moreover, our gearbox dataset was conducted on a fault diagnosis platform, and a series of experiments were conducted to verify the effectiveness and superiority of the MBCNN. …”
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Dual-Space Aggregation Learning and Random Erasure for Visible Infrared Person Re-Identification by Yongheng Qian, Xu Yang, Su-Kit Tang

“…To address these issues, we explore a dual-space aggregation learning (DSAL) method that combines instance-batch normalization (IBN) and residual shrinkage (RS) into a baseline model for feature learning and compression at the channel-level. …”
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Multi-Level Attention Based Coreference Resolution With Gated Recurrent Unit and Convolutional Neural Networks by Bianbadroma, Ngodrup, Erping Zhao, Yuhao Wang, Yakun Zhang

“…The model uses GRU network for global semantic feature learning and knowledge memory, and uses CNN network to further extract local high-level semantic features. …”
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Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion by Jianping Wang, Jian Ma, Dean Meng, Xuan Zhao, Kai Zhang

“…Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. …”
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FilterformerPose: Satellite Pose Estimation Using Filterformer by Ruida Ye, Lifen Wang, Yuan Ren, Yujing Wang, Xiaocen Chen, Yufei Liu

“…To overcome these problems, we introduce a novel satellite pose estimation network, FilterformerPose, which uses a convolutional neural network (CNN) backbone for feature learning and extracts feature maps at various CNN layers. …”
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Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches by Sakorn Mekruksavanich, Anuchit Jitpattanakul

“…Deep learning excels at automated feature learning directly from raw data sans human effort. …”
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Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection by Pramit Dutta, Khaleda Akther Sathi, Md. Azad Hossain, M. Ali Akber Dewan

“…The hybridization of these three models results in shape-based texture feature learning during the classification of retinal diseases into its four classes, including choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. …”
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DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head by Ji Wang, Peiquan Xu, Leijun Li, Feng Zhang

“…First, a multi-branch Dilated Convolution Aggregation Module (DCAM) is proposed as a feature learning structure for the feature augmentation networks. …”
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Advancements in remote sensing: Harnessing the power of artificial intelligence for scene image classification by Alaa O. Khadidos

“…RSSIC, using Deep Learning (DL) techniques, has attracted a considerable attention and accomplished important breakthroughs, thanks to the great feature learning abilities of the Deep Neural Networks (DNNs). …”
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Global and Local Structure Network for Image Classification by Jinping Wang, Ruisheng Ran, Bin Fang

“…Principal component analysis network (PCANet) is a feature learning algorithm that is widely used in face recognition and object classification. …”
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Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition by Chaoyue Li, Lian Zou, Cien Fan, Hao Jiang, Yifeng Liu

“…In addition, a part attention mechanism is proposed to learn the weight of each part and enhance the part-level feature learning. We introduce multiple streams of different stages and merge them in specific layers of the network to further improve the performance of the model. …”
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The Intraday Dynamics Predictor: A TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units for High-Frequency Price Movement Forecasting by Ilia Zaznov, Julian Martin Kunkel, Atta Badii, Alfonso Dufour

“…Key innovations include a tailored input representation incorporating LOB and OF features across recent timestamps, a hierarchical feature-learning architecture leveraging convolutional and recurrent layers, and a model design specifically optimised for LOB and OF data. …”
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Local Matrix Feature-Based Kernel Joint Sparse Representation for Hyperspectral Image Classification by Xiang Chen, Na Chen, Jiangtao Peng, Weiwei Sun

“…The performance of HSI classification greatly depends on the effectiveness of feature learning or feature design. Traditional vector-based spectral–spatial features have shown good performance in HSI classification. …”
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Multi-Scale Residual Spectral–Spatial Attention Combined with Improved Transformer for Hyperspectral Image Classification by Aili Wang, Kang Zhang, Haibin Wu, Yuji Iwahori, Haisong Chen

“…First, in order to efficiently highlight discriminative spectral–spatial information, we propose a multi-scale residual spectral–spatial feature extraction module that preserves the multi-scale information in a two-layer cascade structure, and the spectral–spatial features are refined by residual spectral–spatial attention for the feature-learning stage. In addition, to further capture the sequential spectral relationships, we combine the advantages of Cross-Attention and Re-Attention to alleviate computational burden and attention collapse issues, and propose the Cross-Re-Attention mechanism to achieve an improved transformer, which can efficiently alleviate the heavy memory footprint and huge computational burden of the model. …”
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Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs by Canwei Liu, Xingye Deng, Tingqin He, Lei Chen, Guangyang Deng, Yuanyu Hu

“…Based on the “divide and conquer” strategy, our model divides the global feature learning into multiple separate local intra-view features learning and inter-view features learning processes. …”
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Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning by Rongchuan Yu, Xiaoli Wei, Yan Liu, Fan Yang, Weizheng Shen, Zhixin Gu

“…A CoordAtt attention mechanism and SioU loss function were added to enhance feature learning and training convergence. Multi-scale detection heads were designed to improve small target detection. …”
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Adaptive Kernel Graph Nonnegative Matrix Factorization by Rui-Yu Li, Yu Guo, Bin Zhang

“…Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. …”
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Lightweight Vehicle Detection Based on Improved YOLOv5s by Yuhai Wang, Shuobo Xu, Peng Wang, Kefeng Li, Ze Song, Quanfeng Zheng, Yanshun Li, Qiang He

“…Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. …”
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