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Outline
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Classroom Contents
Thesis Defense: Computing Features in Computer Vision for Event Detection
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- 1 Intro
- 2 Computing features in computer vision
- 3 Hand-Designed Features
- 4 Feature learning problem
- 5 Outline
- 6 Common approach & Challenges
- 7 62 action concepts
- 8 Proposed Method
- 9 Event Detection Process
- 10 Learned Filters
- 11 3D Motion Filters
- 12 Data-driven Low level Features
- 13 Data Driven Concept (2D Scene)
- 14 Data Driven Concept (Motion)
- 15 Summary
- 16 The Model
- 17 Hybrid Learning
- 18 Experimental Setup
- 19 Hybrid Features
- 20 Hybrid Vs. Generative
- 21 Hybrid Vs. Discriminative
- 22 Higher Level Visualization
- 23 Human Detection Results
- 24 Performance on Horse Detection
- 25 Introduction
- 26 Flow Chart
- 27 Gated Auto Encoders - Model the relationship of two videos
- 28 Discriminative Learning
- 29 Pair of Features
- 30 Generative V.S. Discriminative
- 31 K-shot Learning
- 32 Composite dataset
- 33 Computational Cost Comparison
- 34 Conclusion
