Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or bounding box. In this work, we address the need for more informative explanations for these predictors by proposing the post-hoc eXplainable Artificial Intelligence method L-CRP to generate explanations that automatically identify and visualize relevant concepts learned, recognized and used by the model during inference as well as precisely locate them in input space. Our method therefore goes beyond singular input-level attribution maps and, as an approach based on the recently published Concept Relevance Propagation technique, is efficiently applicable to state-of-the-art black-box architectures in segmentation and object detection, such as DeepLabV3+ and YOLOv6, among others. We verify the faithfulness of our proposed technique by quantitatively comparing different concept attribution methods, and discuss the effect on explanation complexity on popular datasets such as CityScapes, Pascal VOC and MS COCO 2017. The ability to precisely locate and communicate concepts is used to reveal and verify the use of background features, thereby highlighting possible biases of the model.
翻译:在对分解或物体探测预测器应用传统的热后分解归因方法只能提供有限的洞察力,因为投入一级获得的特性归因图通常类似于模型预测的分解遮罩或捆绑框。在这项工作中,我们通过提出热后和交织式人工智能法L-CRP来对这些预测器作出更加翔实的解释,以便通过定量比较不同的概念归因方法来核实我们建议的技术的准确性,并讨论对诸如CityScapes、Pascal VOC和MS COCO 2017等流行数据集的解释复杂性的影响。精确定位和沟通概念的能力被用来显示和核实背景的准确性,从而突出和核实可能的背景性。