Learning High-Order Features for Fine-Grained Visual Categorization with Causal Inference

Learning High-Order Features for Fine-Grained Visual Categorization with Causal Inference

Recently, causal models have gained significant attention in natural language processing (NLP) and computer vision (CV) due to their capability of capturing features with causal relationships. This study addresses Fine-Grained Visual Categorization (FGVC) by incorporating high-order feature fusions...

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Bibliographic Details
Main Authors:	Yuhang Zhang, Yuan Wan, Jiahui Hao, Zaili Yang, Huanhuan Li
Format:	Article
Language:	English
Published:	MDPI AG 2025-04-01
Series:	Mathematics
Subjects:	causal models causal inference fine-grained visual categorization feature fusion causal intervention
Online Access:	https://www.mdpi.com/2227-7390/13/8/1340
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