CRCV REU 2019 Kara Schatz

Background Math and Computer Science double major

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

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

What I’ve Learned

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

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

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

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

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

ResNet with varying Dataset Sizes

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

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

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

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

Project: Self-Supervized Cross-View Action Synthesis