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Extraction and Summarization of Explicit Video Content using Multi-Modal Deep Learning

arXiv 2311.10899 ↗First posted 17 Nov 2023cs.CV cs.CL cs.LGcited by 0

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Abstract (arXiv)

With the increase in video-sharing platforms across the internet, it is difficult for humans to moderate the data for explicit content. Hence, an automated pipeline to scan through video data for explicit content has become the need of the hour. We propose a novel pipeline that uses multi-modal deep learning to first extract the explicit segments of input videos and then summarize their content using text to determine its age appropriateness and age rating. We also evaluate our pipeline's effectiveness in the end using standard metrics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 17 Nov 2023.

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10 of 17 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  5. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  6. 2018SlowFast Networks for Video Recognitioncited by 1,867
  7. 2022GIT: A Generative Image-to-text Transformer for Vision and Languagecited by 537
  8. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 238
  9. 2021From None to Severe: Predicting Severity in Movie Scriptscited by 8
  10. 2021VidHarm: A Clip Based Dataset for Harmful Content Detectioncited by 4

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