用地理模型提升物种图像分类准确率
Investigating Different Geo Priors for Image Classification
- 用空间隐式神经网络作地理先验
- 在iNaturalist数据集上提升分类性能
- 适合有地理位置信息的物种识别任务
物种分布模型能编码物种出现的空间模式，当位置信息可用时，可作为视觉分类的有效先验。本研究评估了多种SINR（空间隐式神经表示）模型作为iNaturalist观测数据中物种视觉分类的地理先验效果。我们探索了不同模型配置的影响，并调整了对未包含在地理先验训练中的物种的预测处理方式。分析揭示了这些模型作为地理先验有效性的关键因素，这些因素可能与生成精确分布图所需条件不同。
原文摘要 · Abstract (English)
Species distribution models encode spatial patterns of species occurrence making them effective priors for vision-based species classification when location information is available. In this study, we evaluate various SINR (Spatial Implicit Neural Representations) models as a geographical prior for visual classification of species from iNaturalist observations. We explore the impact of different model configurations and adjust how we handle predictions for species not included in Geo Prior training. Our analysis reveals factors that contribute to the effectiveness of these models as Geo Priors, factors that may differ from making accurate range maps.
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