用新方法选数据，用更少标注实现更高生态图像识别准确率
Vendi Information Gain for Active Learning and its Application to Ecology
- 基于全数据集不确定性选择最具信息量且多样化的图片
- 仅用3%数据达75%准确率，10%数据时准确率达88%
- 适合标注资源少的生态监测场景，可推广至其他领域
通过相机陷阱监测生物多样性已成为生态研究的重要手段，但图像中物种识别仍因标注资源有限而面临瓶颈。主动学习通过选择最具有信息量的数据进行标注和模型训练，提供了可行解决方案，但传统方法通常只关注单个预测的不确定性，未考虑整个数据集的不确定性。本文提出一种新的主动学习策略——Vendi信息增益（VIG），根据图像对全数据集预测不确定性的影响来选择样本，同时捕捉信息量与多样性。我们在Snapshot Serengeti数据集上应用VIG，与主流方法对比显示：仅需3%的标注数据即可达到75%的准确率，而基线方法需要超过10%的数据；使用10%数据时，VIG准确率达到88%，比最优基线高出12%。该性能提升在不同评估指标和批量大小下均保持一致，并且VIG所收集的数据在特征空间中更具多样性。该方法不仅适用于生态学，对数据受限环境下的生物多样性监测具有广泛价值。
原文摘要 · Abstract (English)
While monitoring biodiversity through camera traps has become an important endeavor for ecological research, identifying species in the captured image data remains a major bottleneck due to limited labeling resources. Active learning -- a machine learning paradigm that selects the most informative data to label and train a predictive model -- offers a promising solution, but typically focuses on uncertainty in the individual predictions without considering uncertainty across the entire dataset. We introduce a new active learning policy, Vendi information gain (VIG), that selects images based on their impact on dataset-wide prediction uncertainty, capturing both informativeness and diversity. We applied VIG to the Snapshot Serengeti dataset and compared it against common active learning methods. VIG needs only 3% of the available data to reach 75% accuracy, a level that baselines require more than 10% of the data to achieve. With 10% of the data, VIG attains 88% predictive accuracy, 12% higher than the best of the baselines. This improvement in performance is consistent across metrics and batch sizes, and we show that VIG also collects more diverse data in the feature space. VIG has broad applicability beyond ecology, and our results highlight its value for biodiversity monitoring in data-limited environments.
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