ArticlePloS one2021
Fine-grained classification based on multi-scale pyramid convolution networks.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi scale-aware attention for pyramid convolution network on finger vein recognition.Scientific reports · 2024Article
- Classification ofPlants (Basel, Switzerland) · 2023Article
- Multi-Scale Feature Fusion of Covariance Pooling Networks for Fine-Grained Visual Recognition.Sensors (Basel, Switzerland) · 2023Article
- Instance segmentation convolutional neural network based on multi-scale attention mechanism.PloS one · 2022Article
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Authors and funding
5 authors.
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Abstract
The large intra-class variance and small inter-class variance are the key factor affecting fine-grained image classification. Recently, some algorithms have been more accurate and efficient. However, these methods ignore the multi-scale information of the network, resulting in insufficient ability to capture subtle changes. To solve this problem, a weakly supervised fine-grained classification network based on multi-scale pyramid is proposed in this paper. It uses pyramid convolution kernel to replace ordinary convolution kernel in residual network, which can expand the receptive field of the convolution kernel and use complementary information of different scales. Meanwhile, the weakly supervised data augmentation network (WS-DAN) is used to prevent over fitting and improve the performance of the model. In addition, a new attention module, which includes spatial attention and channel attention, is introduced to pay more attention to the object part in the image. The comprehensive experiments are carried out on three public benchmarks. It shows that the proposed method can extract subtle feature and achieve classification effectively.
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