BSED: Baseline Shapley-Based Explainable Detector

TL;DR

BSED introduces a Shapley value-based approach for object detection explanations, improving validity, efficiency, and generalizability of XAI methods, with practical applications demonstrated.

Contribution

It extends the Shapley value to object detection, ensuring valid, model-agnostic explanations with reasonable computational costs.

Findings

BSED outperforms existing methods in explanation validity.

The method is applicable to various detectors without fine-tuning.

Applications include detection correction based on explanations.

Abstract

Explainable artificial intelligence (XAI) has witnessed significant advances in the field of object recognition, with saliency maps being used to highlight image features relevant to the predictions of learned models. Although these advances have made AI-based technology more interpretable to humans, several issues have come to light. Some approaches present explanations irrelevant to predictions, and cannot guarantee the validity of XAI (axioms). In this study, we propose the Baseline Shapley-based Explainable Detector (BSED), which extends the Shapley value to object detection, thereby enhancing the validity of interpretation. The Shapley value can attribute the prediction of a learned model to a baseline feature while satisfying the explainability axioms. The processing cost for the BSED is within the reasonable range, while the original Shapley value is prohibitively computationally expensive. Furthermore, BSED is a generalizable method that can be applied to various detectors in a model-agnostic manner, and interpret various detection targets without fine-grained parameter tuning. These strengths can enable the practical applicability of XAI. We present quantitative and qualitative comparisons with existing methods to demonstrate the superior performance of our method in terms of explanation validity. Moreover, we present some applications, such as correcting detection based on explanations from our method.