# Where to look at the movies : Analyzing visual attention to understand movie editing

arXiv: https://arxiv.org/abs/2102.13378 · HTML: https://mlsanity.com/paper/2102.13378/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 86 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Revisiting Video Saliency: A Large-scale Benchmark and a New Model](https://mlsanity.com/paper/1801.07424/) (2018)
- [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1701.01081/) (2017)
- [DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations](https://mlsanity.com/paper/1510.02927/) (2015)
- [Contextual Encoder-Decoder Network for Visual Saliency Prediction](https://mlsanity.com/paper/1902.06634/) (2019)
- [Shallow and Deep Convolutional Networks for Saliency Prediction](https://mlsanity.com/paper/1603.00845/) (2016)
- [Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition](https://mlsanity.com/paper/1312.7570/) (2013)
- [Gaze Embeddings for Zero-Shot Image Classification](https://mlsanity.com/paper/1611.09309/) (2016)
- [DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction](https://mlsanity.com/paper/1905.10693/) (2019)
- [The Prose Storyboard Language: A Tool for Annotating and Directing Movies](https://mlsanity.com/paper/1508.07593/) (2015)

## Built on by (most cited first, 2 of 2)

- [Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning](https://mlsanity.com/paper/2310.08032/) (2023)
- [Convolutions Need Registers Too: HVS-Inspired Dynamic Attention for Video Quality Assessment](https://mlsanity.com/paper/2601.11045/) (2026)

## Abstract (arXiv)

In the process of making a movie, directors constantly care about where the spectator will look on the screen. Shot composition, framing, camera movements or editing are tools commonly used to direct attention. In order to provide a quantitative analysis of the relationship between those tools and gaze patterns, we propose a new eye-tracking database, containing gaze pattern information on movie sequences, as well as editing annotations, and we show how state-of-the-art computational saliency techniques behave on this dataset. In this work, we expose strong links between movie editing and spectators scanpaths, and open several leads on how the knowledge of editing information could improve human visual attention modeling for cinematic content. The dataset generated and analysed during the current study is available at https://github.com/abruckert/eye_tracking_filmmaking