Video Action Understanding: A Tutorial
Abstract
Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, the span of video action problems and the set of proposed deep learning solutions is arguably wider and more diverse than those of their 2D image siblings. Finding, identifying, and predicting actions are a few of the most salient tasks in video action understanding. This tutorial clarifies a taxonomy of video action problems, highlights datasets and metrics used to baseline each problem, describes common data preparation methods, and presents the building blocks of state-of-the-art deep learning model architectures.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 13, 2020
- Accession Number
- AD1143531
Entities
People
- Matthew Hutchinson
- Vijay Gadepally
Organizations
- MIT Lincoln Laboratory