# An Integrated Framework for Multi-Granular Explanation of Video Summarization

arXiv: https://arxiv.org/abs/2405.10082 · HTML: https://mlsanity.com/paper/2405.10082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation](https://mlsanity.com/paper/2204.04656/) (2022)
- [Summarizing Videos with Attention](https://mlsanity.com/paper/1812.01969/) (2018)
- [Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge](https://mlsanity.com/paper/1709.09121/) (2017)
- [On the Explainability of Natural Language Processing Deep Models](https://mlsanity.com/paper/2210.06929/) (2022)
- [Video Summarization by Learning from Unpaired Data](https://mlsanity.com/paper/1805.12174/) (2018)
- [Explainable Video Action Reasoning via Prior Knowledge and State Transitions](https://mlsanity.com/paper/1908.10700/) (2019)
- [Exploring global diverse attention via pairwise temporal relation for video summarization](https://mlsanity.com/paper/2009.10942/) (2020)
- [Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions](https://mlsanity.com/paper/1902.01078/) (2019)
- [EVAL: Explainable Video Anomaly Localization](https://mlsanity.com/paper/2212.07900/) (2022)
- [Fast Video Shot Transition Localization with Deep Structured Models](https://mlsanity.com/paper/1808.04234/) (2018)
- [View-invariant Deep Architecture for Human Action Recognition using late fusion](https://mlsanity.com/paper/1912.03632/) (2019)
- [Excitation Backprop for RNNs](https://mlsanity.com/paper/1711.06778/) (2017)
- [Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks](https://mlsanity.com/paper/2002.00367/) (2020)
- [Discrete neural representations for explainable anomaly detection](https://mlsanity.com/paper/2112.05585/) (2021)
- [Causalainer: Causal Explainer for Automatic Video Summarization](https://mlsanity.com/paper/2305.00455/) (2023)
- [Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism](https://mlsanity.com/paper/2209.11189/) (2022)
- [ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network](https://mlsanity.com/paper/2207.09927/) (2022)
- [T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers](https://mlsanity.com/paper/2403.04523/) (2024)
- [Facilitating the Production of Well-tailored Video Summaries for Sharing on Social Media](https://mlsanity.com/paper/2312.02616/) (2023)

## Built on by (most cited first, 1 of 1)

- [An Experimental Study on Generating Plausible Textual Explanations for Video Summarization](https://mlsanity.com/paper/2509.26225/) (2025)

## Abstract (arXiv)

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.