# FFNet: Video Fast-Forwarding via Reinforcement Learning

arXiv: https://arxiv.org/abs/1805.02792 · HTML: https://mlsanity.com/paper/1805.02792/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 13 of 13)

- [Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net](https://mlsanity.com/paper/2106.10528/) (2021)
- [DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization](https://mlsanity.com/paper/2105.06441/) (2021)
- [The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics](https://mlsanity.com/paper/2603.14375/) (2026)
- [How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization](https://mlsanity.com/paper/2101.10514/) (2021)
- [FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics](https://mlsanity.com/paper/2203.11493/) (2022)
- [Collaborative Multi-Agent Video Fast-Forwarding](https://mlsanity.com/paper/2305.17569/) (2023)
- [Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning](https://mlsanity.com/paper/2104.06142/) (2021)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method](https://mlsanity.com/paper/2203.15778/) (2022)
- [CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection](https://mlsanity.com/paper/2203.06814/) (2022)
- [TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning](https://mlsanity.com/paper/2605.01659/) (2026)
- [Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving](https://mlsanity.com/paper/2103.13021/) (2021)
- [EdgeVidSum: Real-Time Personalized Video Summarization at the Edge](https://mlsanity.com/paper/2506.03171/) (2025)

## Abstract (arXiv)

For many applications with limited computation, communication, storage and energy resources, there is an imperative need of computer vision methods that could select an informative subset of the input video for efficient processing at or near real time. In the literature, there are two relevant groups of approaches: generating a trailer for a video or fast-forwarding while watching/processing the video. The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users. In the second group, current fast-forwarding methods depend on either manual control or automatic adaptation of playback speed, which often do not present an accurate representation and may still require processing of every frame. In this paper, we introduce FastForwardNet (FFNet), a reinforcement learning agent that gets inspiration from video summarization and does fast-forwarding differently. It is an online framework that automatically fast-forwards a video and presents a representative subset of frames to users on the fly. It does not require processing the entire video, but just the portion that is selected by the fast-forward agent, which makes the process very computationally efficient. The online nature of our proposed method also enables the users to begin fast-forwarding at any point of the video. Experiments on two real-world datasets demonstrate that our method can provide better representation of the input video with much less processing requirement.