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 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 movie genre identification are limited to subtitles, plot synopses, and movie scenes that are mostly accessible after the movie release. Posters usually contain pre-release implicit information to generate mass interest. In this paper, we work for automated multi-label genre identification only from movie poster images, without any aid of additional textual/meta-data 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 experimental analysis, 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.

翻译：在电影行业中，电影海报数十年来一直是广告和营销的核心组成部分，即便在当下，通过在线平台、社交媒体及OTT平台呈现的数字海报形式仍扮演着至关重要的角色。通常，电影海报能有效宣传并传达电影的精髓，例如其类型、视觉风格/基调、氛围以及故事线索/主题，这些要素对吸引潜在观众至关重要。识别电影类型在将影片推荐给目标受众方面具有显著的实际应用价值。以往关于电影类型识别的研究多局限于字幕、剧情简介和电影场景等通常只能在影片上映后获取的资源。海报往往包含上映前的隐含信息，旨在激发大众兴趣。本文致力于仅凭借电影海报图像实现自动化的多标签类型识别，无需借助任何关于电影的额外文本/元数据信息，这属于该领域最早的尝试之一。我们提出了一种结合概率模块的深度Transformer网络，专门从海报中识别电影类型。为开展实验分析，我们从互联网电影数据库（IMDb）收集了涵盖13种类型的13882张海报。我们的模型表现令人鼓舞，甚至超越了部分主流当代架构。