Li, Xiaoxu;
Wang, Xiangyang;
Zhu, Rui;
Ma, Zhanyu;
Cao, Jie;
Xue, Jing-Hao;
(2024)
Selectively augmented attention network for few-shot image classification.
IEEE Transactions on Circuits and Systems for Video Technology
10.1109/tcsvt.2024.3480279.
Preview |
Text
Selectively_augmented_attention_network_for_few-shot_image_classification.pdf - Accepted Version Download (9MB) | Preview |
Abstract
Few-shot image classification is a challenging task that aims to learn from a limited number of labelled training images a classification model that can be generalised to unseen classes. Two strategies are usually taken to improve the classification performances of few-shot image classifiers: either applying data augmentation to enlarge the sample size of the training set and reduce overfitting, or involving attention mechanisms to highlight discriminative spatial regions or channels. However, naively applying them to few-shot classifiers directly and separately may lead to undesirable results; for example, some augmented images may focus majorly on the background rather than the object, which brings additional noises to the training process. In this paper, we propose a unified framework, the selectively augmented attention (SAA) network, that carefully integrates the best of the two approaches in an end-to-end fashion via a selective best match module to select the most representative images from the augmented training set. The selected images tend to concentrate on the objects with less irrelevant background, which can assist the subsequent calculation of attentions by alleviating the interference from background. Moreover, we design a joint attention module to jointly learn both the spatial and channel-wise attentions. Experimental results on four benchmark datasets showcase the superior classification performance of the proposed SAA network compared with the state-of-the-arts.
| Type: | Article |
|---|---|
| Title: | Selectively augmented attention network for few-shot image classification |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcsvt.2024.3480279 |
| Publisher version: | https://doi.org/10.1109/TCSVT.2024.3480279 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Few-shot image classification, Data augmentation, Attention mechanism, Metric-based methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10198870 |
Archive Staff Only
 |
View Item |
