  Recent research that applies Transformer-based architectures to image
captioning has resulted in state-of-the-art image captioning performance,
capitalising on the success of Transformers on natural language tasks.
Unfortunately, though these models work well, one major flaw is their large
model sizes. To this end, we present three parameter reduction methods for
image captioning Transformers: Radix Encoding, cross-layer parameter sharing,
and attention parameter sharing. By combining these methods, our proposed ACORT
models have 3.7x to 21.6x fewer parameters than the baseline model without
compromising test performance. Results on the MS-COCO dataset demonstrate that
our ACORT models are competitive against baselines and SOTA approaches, with
CIDEr score >=126. Finally, we present qualitative results and ablation studies
to demonstrate the efficacy of the proposed changes further. Code and
pre-trained models are publicly available at
https://github.com/jiahuei/sparse-image-captioning.
