# LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

arXiv: https://arxiv.org/abs/2201.09049 · HTML: https://mlsanity.com/paper/2201.09049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [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)
- [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://mlsanity.com/paper/1609.01388/) (2016)
- [Recurrent Residual Module for Fast Inference in Videos](https://mlsanity.com/paper/1802.09723/) (2018)
- [ODN: Opening the Deep Network for Open-set Action Recognition](https://mlsanity.com/paper/1901.07757/) (2019)
- [Smart Director: An Event-Driven Directing System for Live Broadcasting](https://mlsanity.com/paper/2201.04024/) (2022)
- [Vortex Pooling: Improving Context Representation in Semantic Segmentation](https://mlsanity.com/paper/1804.06242/) (2018)

## Built on by (most cited first, 4 of 4)

- [ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey](https://mlsanity.com/paper/2307.04251/) (2023)
- [Video Summarization Techniques: A Comprehensive Review](https://mlsanity.com/paper/2410.04449/) (2024)
- [EdgeVidSum: Real-Time Personalized Video Summarization at the Edge](https://mlsanity.com/paper/2506.03171/) (2025)
- [SD-MVSum: Script-Driven Multimodal Video Summarization Method and Datasets](https://mlsanity.com/paper/2510.05652/) (2025)

## Abstract (arXiv)

This paper proposes a novel lightweight thumbnail container-based summarization (LTC-SUM) framework for full feature-length videos. This framework generates a personalized keyshot summary for concurrent users by using the computational resource of the end-user device. State-of-the-art methods that acquire and process entire video data to generate video summaries are highly computationally intensive. In this regard, the proposed LTC-SUM method uses lightweight thumbnails to handle the complex process of detecting events. This significantly reduces computational complexity and improves communication and storage efficiency by resolving computational and privacy bottlenecks in resource-constrained end-user devices. These improvements were achieved by designing a lightweight 2D CNN model to extract features from thumbnails, which helped select and retrieve only a handful of specific segments. Extensive quantitative experiments on a set of full 18 feature-length videos (approximately 32.9 h in duration) showed that the proposed method is significantly computationally efficient than state-of-the-art methods on the same end-user device configurations. Joint qualitative assessments of the results of 56 participants showed that participants gave higher ratings to the summaries generated using the proposed method. To the best of our knowledge, this is the first attempt in designing a fully client-driven personalized keyshot video summarization framework using thumbnail containers for feature-length videos.