Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification

• URL: http://arxiv.org/abs/2507.08248v1
• Date: Fri, 11 Jul 2025 01:21:21 GMT
• Title: Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification
• Authors: Jason Kahei Tam, Murilo Gustineli, Anthony Miyaguchi,
• Abstract summary: This paper presents our approach for the FungiCLEF 2025 competition.<n>It focuses on few-shot fine-grained visual categorization using the FungiTastic Few-Shot dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate identification of fungi species presents a unique challenge in computer vision due to fine-grained inter-species variation and high intra-species variation. This paper presents our approach for the FungiCLEF 2025 competition, which focuses on few-shot fine-grained visual categorization (FGVC) using the FungiTastic Few-Shot dataset. Our team (DS@GT) experimented with multiple vision transformer models, data augmentation, weighted sampling, and incorporating textual information. We also explored generative AI models for zero-shot classification using structured prompting but found them to significantly underperform relative to vision-based models. Our final model outperformed both competition baselines and highlighted the effectiveness of domain specific pretraining and balanced sampling strategies. Our approach ranked 35/74 on the private test set in post-completion evaluation, this suggests additional work can be done on metadata selection and domain-adapted multi-modal learning. Our code is available at https://github.com/dsgt-arc/fungiclef-2025.

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