自动发现分割模型的系统性误判并解释原因
Automatic Discovery and Assessment of Interpretable Systematic Errors in Semantic Segmentation
- 用多模态大模型检索错误，通过概念关联分析系统性缺陷
- 在UperNet系列模型上发现大量可解释的误判模式
- 适合关注模型可靠性与可解释性的研究者使用
本文提出一种新方法，用于自动发现分割模型中的系统性错误。例如，模型将停车计时器错误分类为行人，这种错误在自动驾驶等关键应用中尤为危险。现有方法难以在未标注数据上自动识别此类错误，并形成可解释的语义子组以支持干预。为此，我们利用多模态基础模型检索错误实例，结合概念关联与错误特性分析其系统性。实验表明，该方法在基于Berkeley Deep Drive数据集训练的SOTA模型（UperNet ConvNeXt和UperNet Swin）上能有效发现结构一致的系统性误判。研究为语义分割中的模型分析与干预开辟了新路径。
原文摘要 · Abstract (English)
This paper presents a novel method for discovering systematic errors in segmentation models. For instance, a systematic error in the segmentation model can be a sufficiently large number of misclassifications from the model as a parking meter for a target class of pedestrians. With the rapid deployment of these models in critical applications such as autonomous driving, it is vital to detect and interpret these systematic errors. However, the key challenge is automatically discovering such failures on unlabelled data and forming interpretable semantic sub-groups for intervention. For this, we leverage multimodal foundation models to retrieve errors and use conceptual linkage along with erroneous nature to study the systematic nature of these errors. We demonstrate that such errors are present in SOTA segmentation models (UperNet ConvNeXt and UperNet Swin) trained on the Berkeley Deep Drive and benchmark the approach qualitatively and quantitatively, showing its effectiveness by discovering coherent systematic errors for these models. Our work opens up the avenue to model analysis and intervention that have so far been underexplored in semantic segmentation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。