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Video Captioning and Summarization have become very popular in the recentyears due to advancements in Sequence Modelling, with the resurgence ofLong-Short Term Memory networks (LSTMs) and introduction of Gated RecurrentUnits (GRUs). Existing architectures extract spatio-temporal features usingCNNs and utilize either GRUs or LSTMs to model dependencies with soft attentionlayers. These attention layers do help in attending to the most prominentfeatures and improve upon the recurrent units, however, these models sufferfrom the inherent drawbacks of the recurrent units themselves. The introductionof the Transformer model has driven the Sequence Modelling field into a newdirection. In this project, we implement a Transformer-based model for Videocaptioning, utilizing 3D CNN architectures like C3D and Two-stream I3D forvideo extraction. We also apply certain dimensionality reduction techniques soas to keep the overall size of the model within limits. We finally present ourresults on the MSVD and ActivityNet datasets for Single and Dense videocaptioning tasks respectively.

Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers