Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning
This addresses image captioning for AI applications, offering incremental improvements in speed and performance.
The paper tackles image captioning by introducing the Expansion mechanism, which processes sequences without constraints on element count, and reports achieving state-of-the-art results with scores like 143.7 CIDErD on MS COCO 2014 and up to 2.8 times faster end-to-end training.
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