# Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification

arXiv: https://arxiv.org/abs/2309.12022 · HTML: https://mlsanity.com/paper/2309.12022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [Moviescope: Large-scale Analysis of Movies using Multiple Modalities](https://mlsanity.com/paper/1908.03180/) (2019)
- [Search Algorithms for Automated Hyper-Parameter Tuning](https://mlsanity.com/paper/2104.14677/) (2021)
- [A multimodal approach for multi-label movie genre classification](https://mlsanity.com/paper/2006.00654/) (2020)
- [Predicting Movie Genres Based on Plot Summaries](https://mlsanity.com/paper/1801.04813/) (2018)
- [Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network](https://mlsanity.com/paper/1808.04943/) (2018)

## Built on by (most cited first, 1 of 1)

- [Demystifying ChatGPT: How It Masters Genre Recognition](https://mlsanity.com/paper/2507.03875/) (2025)

## Abstract (arXiv)

In the film industry, movie posters have been an essential part of advertising and marketing for many decades, and continue to play a vital role even today in the form of digital posters through online, social media and OTT (over-the-top) platforms. Typically, movie posters can effectively promote and communicate the essence of a film, such as its genre, visual style/tone, vibe and storyline cue/theme, which are essential to attract potential viewers. Identifying the genres of a movie often has significant practical applications in recommending the film to target audiences. Previous studies on genre identification have primarily focused on sources such as plot synopses, subtitles, metadata, movie scenes, and trailer videos; however, posters precede the availability of these sources, and provide pre-release implicit information to generate mass interest. In this paper, we work for automated multi-label movie genre identification only from poster images, without any aid of additional textual/metadata/video information about movies, which is one of the earliest attempts of its kind. Here, we present a deep transformer network with a probabilistic module to identify the movie genres exclusively from the poster. For experiments, we procured 13882 number of posters of 13 genres from the Internet Movie Database (IMDb), where our model performances were encouraging and even outperformed some major contemporary architectures.