  Video captioning is a popular task that challenges models to describe events
in videos using natural language. In this work, we investigate the ability of
various visual feature representations derived from state-of-the-art
convolutional neural networks to capture high-level semantic context. We
introduce the Weighted Additive Fusion Transformer with Memory Augmented
Encoders (WAFTM), a captioning model that incorporates memory in a transformer
encoder and uses a novel method, to fuse features, that ensures due importance
is given to more significant representations. We illustrate a gain in
performance realized by applying Word-Piece Tokenization and a popular
REINFORCE algorithm. Finally, we benchmark our model on two datasets and obtain
a CIDEr of 92.4 on MSVD and a METEOR of 0.091 on the ActivityNet Captions
Dataset.
