{"ID":2895531,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08248","arxiv_id":"2507.08248","title":"Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification","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.","short_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 FungiTa...","url_abs":"https://arxiv.org/abs/2507.08248","url_pdf":"https://arxiv.org/pdf/2507.08248v1","authors":"[\"Jason Kahei Tam\",\"Murilo Gustineli\",\"Anthony Miyaguchi\"]","published":"2025-07-11T01:21:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.IR\",\"cs.LG\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false,"code_links":[{"ID":612203,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2895531,"paper_url":"https://arxiv.org/abs/2507.08248","paper_title":"Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification","repo_url":"https://github.com/dsgt-arc/fungiclef-2025","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}