Demystifying Visual Features of Movie Posters for Multi-Label Genre
  Identification
Utsav Kumar Nareti Chandranath Adak Soumi Chattopadhyay
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.