Abstract
Fine-grained image recognition is more difficult than conventional image classification tasks. Previous advanced network models require a large number of complex structure design and preliminary training. In this paper, a novel attention mechanism: Global Spatial Coordinate Attention Module (GSCAM) is proposed. This structure inherits the advantages of widely used SE channel attention and CBAM spatial attention, uses two 1D features for spatial position coding, and parallels global attention convergence blocks to extract context features, which can obtain the spatial location information and global context feature information of the region of interest of the image, so as to accurately locate the distinguishable region in the image. We apply this module to the mainstream classification network (ResNet50, ResNeXt50) pre-trained by ImageNet. Under the condition that the original network increases a very small number of parameters and computational cost, the accuracy of fine-grained image recognition is close to the SOTA level in different benchmark datasets, and there is no need to design the network structure from scratch, which is simple and fast.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 62071384, Key Research and Development Project of Shaanxi Province under Grant 2020ZDLGY04–09.
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Guo, H., Guo, Z., Pan, Z. (2021). GSCAM: Global Spatial Coordinate Attention Module for Fine-Grained Image Recognition. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_36
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