  The rise of deep learning has ushered in significant progress in computer
vision (CV) tasks, yet the "black box" nature of these models often precludes
interpretability. This challenge has spurred the development of Explainable
Artificial Intelligence (XAI) by generating explanations to AI's
decision-making process. An explanation is aimed to not only faithfully reflect
the true reasoning process (i.e., faithfulness) but also align with humans'
reasoning (i.e., alignment). Within XAI, visual explanations employ visual cues
to elucidate the reasoning behind machine learning models, particularly in
image processing, by highlighting images' critical areas important to
predictions. Despite the considerable body of research in visual explanations,
standardized benchmarks for evaluating them are seriously underdeveloped. In
particular, to evaluate alignment, existing works usually merely illustrate a
few images' visual explanations, or hire some referees to report the
explanation quality under ad-hoc questionnaires. However, this cannot achieve a
standardized, quantitative, and comprehensive evaluation. To address this
issue, we develop a benchmark for visual explanation, consisting of eight
datasets with human explanation annotations from various domains, accommodating
both post-hoc and intrinsic visual explanation methods. Additionally, we devise
a visual explanation pipeline that includes data loading, explanation
generation, and method evaluation. Our proposed benchmarks facilitate a fair
evaluation and comparison of visual explanation methods. Building on our
curated collection of datasets, we benchmarked eight existing visual
explanation methods and conducted a thorough comparison across four selected
datasets using six alignment-based and causality-based metrics. Our benchmark
will be accessible through our website https://xaidataset.github.io.
