  This work demonstrates the implementation and use of an encoder-decoder model
to perform a many-to-many mapping of video data to text captions. The
many-to-many mapping occurs via an input temporal sequence of video frames to
an output sequence of words to form a caption sentence. Data preprocessing,
model construction, and model training are discussed. Caption correctness is
evaluated using 2-gram BLEU scores across the different splits of the dataset.
Specific examples of output captions were shown to demonstrate model generality
over the video temporal dimension. Predicted captions were shown to generalize
over video action, even in instances where the video scene changed
dramatically. Model architecture changes are discussed to improve sentence
grammar and correctness.
