In deep learning, the attention mechanism refers to the tendency to focus on distinct parts when dealing with large volumes of information. It has been widely applied across various application domains. With the advancement of deep neural networks, numerous attention mechanisms emerge. Existing attentio...
Extracting buildings from very high resolution (VHR) images has attracted much attention but is still challenging due to their large varieties in appearance and scale. Convolutional neural networks (CNNs) have shown effective and superior performance in automatically learning high-level and discriminative...
EEG-based emotion recognition via channel-wise attention and self attention. IEEE Trans. Affect. Comput. 2020, 14, 382–393. [Google Scholar] [CrossRef] Jia, Z.; Lin, Y.; Wang, J.; Feng, Z.; Xie, X.; Chen, C. HetEmotionNet: Two-stream heterogeneous graph recurrent neural network...
safety detection; pesticide residues; convolutional neural network; visible/near-infrared spectroscopy; Hami melon1. Introduction Hami melon is one of the famous and special products in Xinjiang, tasting delicious and enjoying the reputation of “the king of melons” [1]. The safety of fruits and...
Wavelet transforms: An introduction. Electron. Commun. Eng. J. 1994, 6, 175–186. [Google Scholar] [CrossRef] Yuan, R.; Lv, Y.; Lu, Z.; Li, S.; Li, H. Robust fault diagnosis of rolling bearing via phase space reconstruction of intrinsic mode functions and neural network under ...
Both consist of an attention mechanism, which first calculates the attention weights along the channel axis. Then, the original features are multiplied by the attention coefficient to obtain the features after attention. 𝐴𝑡𝑡𝑒𝑛(𝐹)=𝑀𝑢𝑙(𝐹,𝐶𝑜𝑛(𝐴𝑣𝑔𝑃𝑜...
Recently, sparse representation classification (SRC) has also attracted much attention for the classification of the HSI [27,32,33,34,35]. The SRC assumes that a test pixel can be approximately represented by a linear combination of all training samples. The class label of the test pixel is ...
Hierarchical Multi-Attention Transfer for Knowledge Distillation. ACM Trans. Multim. Comput. Commun. Appl. 2024, 20, 51:1–51:20. [Google Scholar] [CrossRef] Chen, X.; Su, J.; Zhang, J. A Two-Teacher Framework for Knowledge Distillation. In Proceedings of the Advances in Neural Networks...
load state of a wet ball mill during the grinding process, a method of mill load identification based on improved empirical wavelet transform (EWT), multiscale fuzzy entropy (MFE), and adaptive evolution particle swarm optimization probabilistic neural network (AEPSO_PNN) classification is proposed....