MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

We propose Integrated Gradients and Object Detector Activation Maps (IG-ODAM), a novel model that integrates the strengths of ODAM and Integrated Gradients to enhance the interpretability of object detection models. While ODAM effectively visualizes the decision-making processes of object detectors, it does not fully address the limitations of gradient-based attribution identified in prior work. To overcome this, IG-ODAM incorporates the path-integrated computation of Integrated Gradients into ODAM, yielding more accurate and robust visual explanations. This study represents the first application of the Integrated Gradients methodology to object detection, extending its success in image classification to the more complex domain of instance-aware detection. By leveraging both object specification and discriminative power, IG-ODAM produces high-quality, instance-specific visual explanations that satisfy sensitivity and implementation invariance axioms, thereby improving both interpretability and localization accuracy.