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ORIGINAL RESEARCH article
Front. Signal Process.
Sec. Image Processing
Volume 4 - 2024 |
doi: 10.3389/frsip.2024.1433388
This article is part of the Research Topic Explainable, Trustworthy, and Responsible AI in Image Processing View all 4 articles
An Integrated Framework for Multi-Granular Explanation of Video Summarization
Provisionally accepted- Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki, Greece
In this paper, we propose an integrated framework for multi-granular explanation of video summarization. This framework integrates methods for producing explanations both at the fragment level (indicating which video fragments influenced the most the decisions of the summarizer) and the more fine-grained visual object level (highlighting which visual objects were the most influential for the summarizer). To build this framework, we extend our previous work on this field, by investigating the use of a model-agnostic, perturbation-based approach for fragment-level explanation of the video summarization results, and introducing a new method that combines the results of video panoptic segmentation with an adaptation of a perturbation-based explanation approach to produce object-level explanations. The performance of the developed framework is evaluated using a state-of-the-art summarization method and two datasets for benchmarking video summarization. The findings of the conducted quantitative and qualitative evaluations demonstrate the ability of our framework to spot the most and least influential fragments and visual objects of the video for the summarizer, and to provide a comprehensive set of visual-based explanations about the output of the summarization process.
Keywords: Explainable AI, video summarization, Fragment-level explanation, Object-level explanation, Model-specific explanation method, Model-agnostic explanation method, Quantitative evaluation, qualitative evaluation
Received: 15 May 2024; Accepted: 02 Dec 2024.
Copyright: © 2024 Tsigos, Apostolidis and Mezaris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Evlampios Apostolidis, Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki, 57001, Greece
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