Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation

Traditional fine-grained image classification typically relies on large-scale training samples with annotated ground-truth. However, some sub-categories have few available samples in real-world applications, and current few-shot models still have difficulty in distinguishing subtle differences among...

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Veröffentlicht in:	arXiv.org 2022-10
Hauptverfasser:	Zhu, Hegui, Gao, Zhan, Wang, Jiayi, Zhou, Yange, Li, Chengqing
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification Cross modulation Datasets Image classification Self-similarity
Online-Zugang:	Volltext
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