May 8, 2024, 4:45 a.m. | Da Fu, Mingfei Rong, Eun-Hu Kim, Hao Huang, Witold Pedrycz

cs.CV updates on

arXiv:2405.04093v1 Announce Type: new
Abstract: Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms. This study proposes novel dual-current neural networks (DCNN), which combine the advantages of convolutional operations and self-attention mechanisms to improve the accuracy of fine-grained image classification. The main novel design features for constructing a weakly supervised learning backbone model DCNN include (a) extracting heterogeneous data, (b) keeping the feature map resolution unchanged, (c) expanding the receptive field, and …

abstract accuracy advantages arxiv attention attention mechanisms challenge classification convolutional current deep learning fine-grained images interactive networks neural networks novel objects operations self-attention study type

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