  Explainable artificial intelligence techniques are becoming increasingly
important with the rise of deep learning applications in various domains. These
techniques aim to provide a better understanding of complex "black box" models
and enhance user trust while maintaining high learning performance. While many
studies have focused on explaining deep learning models in computer vision for
image input, video explanations remain relatively unexplored due to the
temporal dimension's complexity. In this paper, we present a unified framework
for local agnostic explanations in the video domain. Our contributions include:
(1) Extending a fine-grained explanation framework tailored for computer vision
data, (2) Adapting six existing explanation techniques to work on video data by
incorporating temporal information and enabling local explanations, and (3)
Conducting an evaluation and comparison of the adapted explanation methods
using different models and datasets. We discuss the possibilities and choices
involved in the removal-based explanation process for visual data. The
adaptation of six explanation methods for video is explained, with comparisons
to existing approaches. We evaluate the performance of the methods using
automated metrics and user-based evaluation, showing that 3D RISE, 3D LIME, and
3D Kernel SHAP outperform other methods. By decomposing the explanation process
into manageable steps, we facilitate the study of each choice's impact and
allow for further refinement of explanation methods to suit specific datasets
and models.
