mlsanityData to 7 Oct 2026

Deep Architectures for Content Moderation and Movie Content Rating

arXiv 2212.04533 ↗First posted 8 Dec 2022cs.CV cs.LGcited by 2

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Abstract (arXiv)

Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 49 of its 87 references that are in the map. First posted on arXiv 8 Dec 2022.

Builds on

49 of 87 references.

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Cited by year

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

2 papers.

  1. 2025QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Designcited by 7
  2. 2023State-of-the-Art in Nudity Classification: A Comparative Analysiscited by 3