Summarizing and Searching Video: Domain Adaptation
Abstract
Despite impressive improvements in machine learning systems in recent years, classifiers still struggle to perform when there is little or no training data in the target environment. Semantic differences, such as perspective and object density, between source and target environments can significantly degrade classifier accuracy. Non-semantic differences, such as differences in object environment, can significantly degrade classifier accuracy. Differences between the trained and real world data sets also hamper classifier performance.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2019
- Accession Number
- AD1118406
Entities
Organizations
- Carnegie Mellon University