Markerless tracking of user-defined features with deep learning

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming... (read more)

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
ReLU
Activation Functions
Residual Connection
Skip Connections
Max Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
Bottleneck Residual Block
Skip Connection Blocks
Residual Block
Skip Connection Blocks
Kaiming Initialization
Initialization
Convolution
Convolutions
ResNet
Convolutional Neural Networks