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Explainable artificial intelligence techniques are becoming increasinglyimportant with the rise of deep learning applications in various domains. Thesetechniques aim to provide a better understanding of complex "black box" modelsand enhance user trust while maintaining high learning performance. While manystudies have focused on explaining deep learning models in computer vision forimage input, video explanations remain relatively unexplored due to thetemporal dimension's complexity. In this paper, we present a unified frameworkfor local agnostic explanations in the video domain. Our contributions include:(1) Extending a fine-grained explanation framework tailored for computer visiondata, (2) Adapting six existing explanation techniques to work on video data byincorporating temporal information and enabling local explanations, and (3)Conducting an evaluation and comparison of the adapted explanation methodsusing different models and datasets. We discuss the possibilities and choicesinvolved in the removal-based explanation process for visual data. Theadaptation of six explanation methods for video is explained, with comparisonsto existing approaches. We evaluate the performance of the methods usingautomated metrics and user-based evaluation, showing that 3D RISE, 3D LIME, and3D Kernel SHAP outperform other methods. By decomposing the explanation processinto manageable steps, we facilitate the study of each choice's impact andallow for further refinement of explanation methods to suit specific datasetsand models.

Local Agnostic Video Explanations: a Study on the Applicability of Removal-Based Explanations to Video