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Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification
Citations:
14
References:
43
Abstract
Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classification tasks while reducing trivial and non-generalizable sample level noise introduced in this procedu...
Keywords
Sustainable Development Goals (SDG)
2024
Article
Open Access
Institution
2-Y Citation Average
5.871
Language
Country
Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification
Citations:
14
References:
43
Abstract
Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classification tasks while reducing trivial and non-generalizable sample level noise introduced in this procedu...
Keywords
Sustainable Development Goals (SDG)
2024
Article
Open Access
Institution
2-Y Citation Average
5.871
Language
Country