Learning a Latent Space with Triplet Network for Few-Shot Image Classification

Jiaying Wu, Jinglu Hu

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Few-shot image classification has attracted much attention due to its requirement of limited training data for target classes. Existing methods usually pretrain a network with images from the base set as feature extractor to obtain features of images from novel set. However, the pretrained feature extractor cannot extract accurate representation for categories have never seen, making images from novel set difficult to distinguish. To be specific, in the pretrained feature space, there exist a large number of overlapped areas between novel categories. To address this issue, it is crucial to acquire a space, where features from same class are gathering together and features from different classes are far away from each other. Since lots of experiments have proved that the triplet network is effective to achieve this goal, in this paper, we base our network on the Maximum a posteriori (MAP), learning a latent space with triplet network to project features from pretrained feature space into a more discriminative one. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods, improves around 1.09%∼13.09% than the best results in each dataset on both 1- and 5-shot tasks.

本文言語English
ホスト出版物のタイトル2022 26th International Conference on Pattern Recognition, ICPR 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5038-5044
ページ数7
ISBN(電子版)9781665490627
DOI
出版ステータスPublished - 2022
イベント26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
継続期間: 2022 8月 212022 8月 25

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
2022-August
ISSN(印刷版)1051-4651

Conference

Conference26th International Conference on Pattern Recognition, ICPR 2022
国/地域Canada
CityMontreal
Period22/8/2122/8/25

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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