mlsanityData to 14 Oct 2026

A Survey of Task-Based Machine Learning Content Extraction Services for VIDINT

arXiv 2207.04158 ↗First posted 9 Jul 2022cs.ET cs.CV cs.CYcited by 0

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Abstract (arXiv)

This paper provides a comparison of current video content extraction tools with a focus on comparing commercial task-based machine learning services. Video intelligence (VIDINT) data has become a critical intelligence source in the past decade. The need for AI-based analytics and automation tools to extract and structure content from video has quickly become a priority for organizations needing to search, analyze and exploit video at scale. With rapid growth in machine learning technology, the maturity of machine transcription, machine translation, topic tagging, and object recognition tasks are improving at an exponential rate, breaking performance records in speed and accuracy as new applications evolve. Each section of this paper reviews and compares products, software resources and video analytics capabilities based on tasks relevant to extracting information from video with machine learning techniques.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 27 references that are in the map. First posted on arXiv 9 Jul 2022.

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References · 2

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811

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