  Explainability in artificial intelligence (XAI) remains a crucial aspect for
fostering trust and understanding in machine learning models. Current visual
explanation techniques, such as gradient-based or class-activation-based
methods, often exhibit a strong dependence on specific model architectures.
Conversely, perturbation-based methods, despite being model-agnostic, are
computationally expensive as they require evaluating models on a large number
of forward passes. In this work, we introduce Foveation-based Explanations
(FovEx), a novel XAI method inspired by human vision. FovEx seamlessly
integrates biologically inspired perturbations by iteratively creating foveated
renderings of the image and combines them with gradient-based visual
explorations to determine locations of interest efficiently. These locations
are selected to maximize the performance of the model to be explained with
respect to the downstream task and then combined to generate an attribution
map. We provide a thorough evaluation with qualitative and quantitative
assessments on established benchmarks. Our method achieves state-of-the-art
performance on both transformers (on 4 out of 5 metrics) and convolutional
models (on 3 out of 5 metrics), demonstrating its versatility among various
architectures. Furthermore, we show the alignment between the explanation map
produced by FovEx and human gaze patterns (+14\% in NSS compared to RISE,
+203\% in NSS compared to GradCAM). This comparison enhances our confidence in
FovEx's ability to close the interpretation gap between humans and machines.
