Li, Xiaoxu;
Song, Qi;
Wu, Jijie;
Zhu, Rui;
Ma, Zhanyu;
Xue, Jing-Hao;
(2023)
Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification.
IEEE Transactions on Circuits and Systems for Video Technology
10.1109/tcsvt.2023.3275382.
(In press).
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Abstract
Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: https://github.com/lutsong/LCCRN.
| Type: | Article |
|---|---|
| Title: | Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcsvt.2023.3275382 |
| Publisher version: | https://doi.org/10.1109/TCSVT.2023.3275382 |
| 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: | Image reconstruction, Feature extraction, Task analysis, Measurement, Image classification, Aircraft, Semantics |
| 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-pp.ucl.ac.uk/id/eprint/10169852 |
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