@inproceedings{f49ba4345f1240429708d54ad7a0f5b3,
title = "Strengthening Spatial Relations to Multi-scale Features for Few-Shot Learning",
abstract = "Few-Shot Learning (FSL) is challenging due to limited training samples, hindering Convolutional Neural Networks (CNNs) from capturing discriminative object features. Recent approaches combine transfer learning and meta-learning, pre-training feature backbones on labeled base data and fine-tuning on novel data. In this work, we propose a CNN architecture with a cross-scale view of objects and introduce a spatial attention module to mitigate the impact of global average pooling. Our method achieves state-of-the-art results on standard benchmark for the cross-domain (CDFSL) few-shot task, demonstrating its effectiveness.",
keywords = "Few-Shot Learning, cross-domain few-shot tasks, meta-learning, transfer learning",
author = "Hsieh, {Yi Kuan} and Hsieh, {Jun Wei} and Chen, {Ying Yu} and Tseng, {Yu Chee}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Conference on Computer Communication and the Internet, ICCCI 2023 ; Conference date: 23-06-2023 Through 25-06-2023",
year = "2023",
doi = "10.1109/ICCCI59363.2023.10210176",
language = "English",
series = "2023 5th International Conference on Computer Communication and the Internet, ICCCI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "56--59",
booktitle = "2023 5th International Conference on Computer Communication and the Internet, ICCCI 2023",
address = "美國",
}