# A Review of Machine Learning Methods Applied to Video Analysis Systems

arXiv: https://arxiv.org/abs/2312.05352 · HTML: https://mlsanity.com/paper/2312.05352/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [Consistency Regularization for Generative Adversarial Networks](https://mlsanity.com/paper/1910.12027/) (2019)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [Self-Supervised Learning for Videos: A Survey](https://mlsanity.com/paper/2207.00419/) (2022)
- [VideoSSL: Semi-Supervised Learning for Video Classification](https://mlsanity.com/paper/2003.00197/) (2020)
- [Zero-Shot Action Recognition in Videos: A Survey](https://mlsanity.com/paper/1909.06423/) (2019)
- [Fast Hand Detection in Collaborative Learning Environments](https://mlsanity.com/paper/2110.07070/) (2021)
- [Active Learning for Video Classification with Frame Level Queries](https://mlsanity.com/paper/2307.05587/) (2023)

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges](https://mlsanity.com/paper/2507.02074/) (2025)
- [How Does India Cook Biryani?](https://mlsanity.com/paper/2601.06198/) (2026)
- [A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video](https://mlsanity.com/paper/2602.09154/) (2026)

## Abstract (arXiv)

The paper provides a survey of the development of machine-learning techniques for video analysis. The survey provides a summary of the most popular deep learning methods used for human activity recognition. We discuss how popular architectures perform on standard datasets and highlight the differences from real-life datasets dominated by multiple activities performed by multiple participants over long periods. For real-life datasets, we describe the use of low-parameter models (with 200X or 1,000X fewer parameters) that are trained to detect a single activity after the relevant objects have been successfully detected. Our survey then turns to a summary of machine learning methods that are specifically developed for working with a small number of labeled video samples. Our goal here is to describe modern techniques that are specifically designed so as to minimize the amount of ground truth that is needed for training and testing video analysis systems. We provide summaries of the development of self-supervised learning, semi-supervised learning, active learning, and zero-shot learning for applications in video analysis. For each method, we provide representative examples.