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1
Intro
2
Problem Statement
3
Results
4
Problem
5
Approaches
6
Privacy leakage
7
Pseudoleveling
8
Contrasting
9
Local Local Contrast Loss
10
Zero Shot Learning
11
Evaluation
12
Cell Survey
13
Experimental Results
14
Qualitative Results
Description:
Explore cutting-edge approaches to Human Activity Recognition in this keynote talk from SPIE Automatic Target Recognition XXXII. Delve into innovative techniques for learning with limited labeled data and preserving privacy. Examine problem statements, results, and various approaches including privacy leakage prevention, pseudoleveling, and contrastive learning. Discover the potential of Zero Shot Learning and Local Local Contrast Loss in this field. Analyze experimental and qualitative results from cell surveys, gaining valuable insights into the future of activity recognition technology.

Human Activity Recognition - Learning with Less Labels and Privacy Preservation

University of Central Florida
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