Summary of Rnns, Cnns and Transformers in Human Action Recognition: a Survey and a Hybrid Model, by Khaled Alomar and Halil Ibrahim Aysel and Xiaohao Cai
RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model
by Khaled Alomar, Halil Ibrahim Aysel, Xiaohao Cai
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides an overview of human action recognition (HAR), a task that involves monitoring human activities across various domains such as medical, educational, entertainment, visual surveillance, video retrieval, and identifying anomalous activities. The research highlights the advancements made in HAR over the past decade, particularly through the use of Convolutional Neural Networks (CNNs) to extract intricate information and enhance performance. The emergence of Vision Transformers (ViTs) is also discussed, showcasing their potential for diverse video-related tasks. As HAR has gained widespread adoption across various domains, this paper aims to survey the evolution of CNNs and Recurrent Neural Networks (RNNs) to ViTs in the context of HAR. It conducts a critical analysis of existing literature, explores emerging trends, and presents a novel hybrid model that integrates the strengths of CNNs and ViTs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can recognize human actions, like walking or dancing, from videos. This is important because it can help us in many areas, such as medicine, education, and entertainment. Over the past 10 years, computer scientists have made big progress by using special kinds of artificial intelligence called Convolutional Neural Networks (CNNs). Recently, another type of AI called Vision Transformers (ViTs) has been developed, which is also very good at understanding videos. The researchers are interested in how to use these different types of AI together to make better computers that can recognize human actions. |
