  We introduce a method called the Expansion mechanism that processes the input
unconstrained by the number of elements in the sequence. By doing so, the model
can learn more effectively compared to traditional attention-based approaches.
To support this claim, we design a novel architecture ExpansionNet v2 that
achieved strong results on the MS COCO 2014 Image Captioning challenge and the
State of the Art in its respective category, with a score of 143.7 CIDErD in
the offline test split, 140.8 CIDErD in the online evaluation server and 72.9
AllCIDEr on the nocaps validation set. Additionally, we introduce an End to End
training algorithm up to 2.8 times faster than established alternatives. Source
code available at: https://github.com/jchenghu/ExpansionNet_v2
