  We examined whether embedding human attention knowledge into saliency-based
explainable AI (XAI) methods for computer vision models could enhance their
plausibility and faithfulness. We first developed new gradient-based XAI
methods for object detection models to generate object-specific explanations by
extending the current methods for image classification models. Interestingly,
while these gradient-based methods worked well for explaining image
classification models, when being used for explaining object detection models,
the resulting saliency maps generally had lower faithfulness than human
attention maps when performing the same task. We then developed Human
Attention-Guided XAI (HAG-XAI) to learn from human attention how to best
combine explanatory information from the models to enhance explanation
plausibility by using trainable activation functions and smoothing kernels to
maximize XAI saliency map's similarity to human attention maps. While for image
classification models, HAG-XAI enhanced explanation plausibility at the expense
of faithfulness, for object detection models it enhanced plausibility and
faithfulness simultaneously and outperformed existing methods. The learned
functions were model-specific, well generalizable to other databases.
