1. CRCV REU 2019
Kara Schatz

2. Background
Math and Computer Science double major

3. Background Math and Computer Science double major Languages: Python
Java
C++
Scheme

4. Background Math and Computer Science double major Languages:
Python
Java
C++
Scheme
Computer Vision:
General idea of Neural Networks

5. What I’ve Learned

6. What I’ve Learned Theoretical Convolution Boosting Neural Networks
Activation Functions
Gradient Descent
Famous CNN Architectures
Optical Flow
Deep Learning

7. What I’ve Learned Theoretical Implementation Convolution Boosting
Neural Networks
Activation Functions
Gradient Descent
Famous CNN Architectures
Optical Flow
Deep Learning
Keras
VGG
ResNet
Pytorch
C3D
I3D

8. Classification on cifar100
Networks Used
3 layer CNN
4 layer CNN
VGG
ResNet

9. Classification on cifar100
Best: VGG
24.1% Validation Accuracy
51.2% Top 5 Validation Accuracy
3.304 Loss
Using training dataset with
75 samples per class

10. Classification on cifar100
Best: VGG
24.1% Validation Accuracy
51.2% Top 5 Validation Accuracy
3.304 Loss
Using training dataset with
75 samples per class

11. ResNet with varying Dataset Sizes

12. Project: Self-Supervized Cross-View Action Synthesis
Dr. Yogesh Rawat

13. Project: Self-Supervized Cross-View Action Synthesis
Datasets:

14. Project: Self-Supervized Cross-View Action Synthesis
Datasets:
NTU-RGBD
60K videos
3 views

15. Project: Self-Supervized Cross-View Action Synthesis
Datasets:
NTU-RGBD
60K videos
3 views
Datasets:
Panoptic dataset
65 video sequences
~540 viewpoints

16. Project: Self-Supervized Cross-View Action Synthesis