  Video captioning works on the two fundamental concepts, feature detection and
feature composition. While modern day transformers are beneficial in composing
features, they lack the fundamental problems of selecting and understanding of
the contents. As the feature length increases, it becomes increasingly
important to include provisions for improved capturing of the pertinent
contents. In this work, we have introduced a new concept of Self-Aware
Composition Transformer (SACT) that is capable of generating Multinomial
Attention (MultAtt) which is a way of generating distributions of various
combinations of frames. Also, multi-head attention transformer works on the
principle of combining all possible contents for attention, which is good for
natural language classification, but has limitations for video captioning.
Video contents have repetitions and require parsing of important contents for
better content composition. In this work, we have introduced SACT for more
selective attention and combined them for different attention heads for better
capturing of the usable contents for any applications. To address the problem
of diversification and encourage selective utilization, we propose the
Self-Aware Composition Transformer model for dense video captioning and apply
the technique on two benchmark datasets like ActivityNet and YouCookII.
