mlsanityData to 7 Oct 2026

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

arXiv 2102.13378 ↗First posted 26 Feb 2021cs.CVcited by 2

Not on the map. Why?

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

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 86 references that are in the map. First posted on arXiv 26 Feb 2021.

Builds on

13 of 86 references.

References · 13

  1. 2018YOLOv3: An Incremental Improvementcited by 2,652
  2. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  3. 2020MovieNet: A Holistic Dataset for Movie Understandingcited by 206
  4. 2016Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Modelcited by 91
  5. 2018Revisiting Video Saliency: A Large-scale Benchmark and a New Modelcited by 66
  6. 2017SalGAN: Visual Saliency Prediction with Generative Adversarial Networkscited by 63
  7. 2015DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixationscited by 47
  8. 2019Contextual Encoder-Decoder Network for Visual Saliency Predictioncited by 44
  9. 2016Shallow and Deep Convolutional Networks for Saliency Predictioncited by 42
  10. 2013Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognitioncited by 36
  11. 2016Gaze Embeddings for Zero-Shot Image Classificationcited by 25
  12. 2019DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Predictioncited by 21
1 more
  1. 2015The Prose Storyboard Language: A Tool for Annotating and Directing Moviescited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.