# A Stacking Ensemble Approach for Supervised Video Summarization

arXiv: https://arxiv.org/abs/2109.12581 · HTML: https://mlsanity.com/paper/2109.12581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 26 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [Summarizing Videos with Attention](https://mlsanity.com/paper/1812.01969/) (2018)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Discriminative Feature Learning for Unsupervised Video Summarization](https://mlsanity.com/paper/1811.09791/) (2018)

## Abstract (arXiv)

Video summarization methods are usually classified into shot-level or frame-level methods, which are individually used in a general way. This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a stacking ensemble approach is proposed for supervised video summarization. Firstly, we build up a stacking model to predict both the key frame probabilities and the temporal interest segments simultaneously. The two components are then combined via soft decision fusion to obtain the final scores of each frame in the video. A joint loss function is proposed for the model training. The ablation experimental results show that the proposed method outperforms both the two corresponding individual method. Furthermore, extensive experimental results on two benchmark datasets shows its superior performance in comparison with the state-of-the-art methods.