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ExpansionNet: exploring the sequence length bottleneck in the Transformer for Image Captioning. (arXiv:2207.03327v3 [cs.CV] UPDATED)
Aug. 16, 2022, 1:13 a.m. | Jia Cheng Hu, Roberto Cavicchioli, Alessandro Capotondi
cs.CV updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.CV updates on arXiv.org
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