提出ARLA方法，动态调整标签分辨率以更准确评估噪声标签下的分割模型。
A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA)
- 在推理时动态调整标签与预测的分辨率，减少标签噪声影响。
- 在洪水预测任务中显著改善森林区域和云层覆盖区的评估结果。
- 适合评估弱标签或不一致标签场景下的模型性能，可调参数适配噪声水平。
标签对深度学习分割模型的训练与评估至关重要，但常在类别边界处出现不一致、噪声或模糊问题。尽管已有多种方法用于弱标签训练，但针对不可靠标签的评估方法仍十分有限。为此，我们提出自适应分辨率标签聚合（Adaptive Resolution Label Aggregation, ARLA），在计算评估指标前，动态调整推理时标签与模型预测的分辨率。实验表明，ARLA能有效克服真实洪水预测模型中林地标注不一致及重云覆盖区域标签错误的问题。该方法通过调节聚合分辨率，适配标签精度或噪声程度，充分挖掘标签中的信息并最小化误差，从噪声中提取出模型真实性能的清晰信号。
原文摘要 · Abstract (English)
Labels are critical for both training and evaluating deep learning segmentation models, but are often inconsistent, noisy, or ambiguous at class boundaries. Many approaches have been developed to support training models on weak labels, but few to none currently exist to facilitate evaluating models on unreliable labels. We therefore introduce a method called "Adaptive Resolution Label Aggregation", or "ARLA", which dynamically adapts the resolution of both the label and the model prediction at inference time before the evaluation metrics are computed. We demonstrate how ARLA can be used to better analyse model behaviour with a practical application to a real flood prediction model, where ARLA was able to overcome issues with inconsistent labelling of forested areas and errors in labels within regions of heavy cloud cover. Our work presents a new approach to evaluating segmentation models, with adjustable parameters to adapt the aggregated resolution to the precision of the label or the level of label noise. Fundamentally, ARLA exploits the information encapsulated by a label but minimises the label error, extracting from the noise a clearer signal of a model's true performance.
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