用视觉模型与数据均衡提升真菌细粒度少样本分类准确率
Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification
- 采用视觉变换器结合平衡采样和数据增强
- 在FungiTastic数据集上超越基线，排名35/74
- 适合对少样本细粒度识别感兴趣的研究者
由于真菌物种间细微差异和种内高度变异性，其精准识别在计算机视觉中极具挑战。本文针对FungiCLEF 2025竞赛，聚焦使用FungiTastic Few-Shot数据集的少样本细粒度视觉分类（FGVC）。我们团队（DS@GT）尝试了多种视觉变换器模型、数据增强、加权采样及文本信息融合策略。同时探索生成式AI模型通过结构化提示实现零样本分类，但发现其表现显著低于基于视觉的模型。最终模型在私有测试集上排名35/74（赛后评估），表明元数据选择与领域自适应多模态学习仍有优化空间。代码已开源：https://github.com/dsgt-arc/fungiclef-2025。
原文摘要 · Abstract (English)
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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