Abstract
Few-shot learning aims to use limited labeled samples to achieve effective classification results. To mine the features of images in a limited number of pieces, some researchers proposed to drill salient features to improve the classification effect. However, they ignore the use of salient secondary features. Therefore, we offer to use salient secondary features to supplement the deficiency of salient features. Combining with the foreground extraction network and the graph neural network, a better classification effect is obtained in the experiment.
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Acknowledgements
This research is sponsored by Natural Science Foundation of Chongqing (cstc2018jscx-mszdX0116), China.
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Liu, C., Hou, W., Wang, X., Zhang, X. (2023). Secondary Salient Feature-Based GNN for Few-Shot Classification. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.H. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2022. Lecture Notes in Electrical Engineering, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-99-1252-0_7
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DOI: https://doi.org/10.1007/978-981-99-1252-0_7
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