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ExpansionNet: exploring the sequence length bottleneck in the Transformer for Image Captioning

2022-07-07 14:37:02
Jia Cheng Hu

Abstract

Most recent state of art architectures rely on combinations and variations of three approaches: convolutional, recurrent and self-attentive methods. Our work attempts in laying the basis for a new research direction for sequence modeling based upon the idea of modifying the sequence length. In order to do that, we propose a new method called ``Expansion Mechanism'' which transforms either dynamically or statically the input sequence into a new one featuring a different sequence length. Furthermore, we introduce a novel architecture that exploits such method and achieves competitive performances on the MS-COCO 2014 data set, yielding 134.6 and 131.4 CIDEr-D on the Karpathy test split in the ensemble and single model configuration respectively and 130 CIDEr-D in the official online testing server, despite being neither recurrent nor fully attentive. At the same time we address the efficiency aspect in our design and introduce a convenient training strategy suitable for most computational resources in contrast to the standard one. Source code is available at this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2207.03327

PDF

https://arxiv.org/pdf/2207.03327.pdf


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