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DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects
May 8, 2024, 4:45 a.m. | Da Fu, Mingfei Rong, Eun-Hu Kim, Hao Huang, Witold Pedrycz
cs.CV updates on arXiv.org arxiv.org
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 cs.ai cs.cv current deep learning fine-grained images interactive networks neural networks novel objects operations self-attention study type
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