# Unraveling Movie Genres through Cross-Attention Fusion of Bi-Modal Synergy of Poster

arXiv: https://arxiv.org/abs/2410.19764 · HTML: https://mlsanity.com/paper/2410.19764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 20 of 20)

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- [CentralNet: a Multilayer Approach for Multimodal Fusion](https://mlsanity.com/paper/1808.07275/) (2018)
- [Moviescope: Large-scale Analysis of Movies using Multiple Modalities](https://mlsanity.com/paper/1908.03180/) (2019)
- [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)
- [Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre Classification](https://mlsanity.com/paper/2210.07983/) (2022)

## Abstract (arXiv)

Movie posters are not just decorative; they are meticulously designed to capture the essence of a movie, such as its genre, storyline, and tone/vibe. For decades, movie posters have graced cinema walls, billboards, and now our digital screens as a form of digital posters. Movie genre classification plays a pivotal role in film marketing, audience engagement, and recommendation systems. Previous explorations into movie genre classification have been mostly examined in plot summaries, subtitles, trailers and movie scenes. Movie posters provide a pre-release tantalizing glimpse into a film's key aspects, which can ignite public interest. In this paper, we presented the framework that exploits movie posters from a visual and textual perspective to address the multilabel movie genre classification problem. Firstly, we extracted text from movie posters using an OCR and retrieved the relevant embedding. Next, we introduce a cross-attention-based fusion module to allocate attention weights to visual and textual embedding. In validating our framework, we utilized 13882 posters sourced from the Internet Movie Database (IMDb). The outcomes of the experiments indicate that our model exhibited promising performance and outperformed even some prominent contemporary architectures.