Semi-Autoregressive Transformer for Image Captioning

• URL: http://arxiv.org/abs/2106.09436v1
• Date: Thu, 17 Jun 2021 12:36:33 GMT
• Title: Semi-Autoregressive Transformer for Image Captioning
• Authors: Yuanen Zhou, Yong Zhang, Zhenzhen Hu, Meng Wang
• Abstract summary: We introduce a semi-autoregressive model for image captioning(dubbed as SATIC) It keeps the autoregressive property in global but generates words parallelly in local. Experiments on the MSCOCO image captioning benchmark show that SATIC can achieve a better trade-off without bells and whistles.
• Score: 17.533503295862808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current state-of-the-art image captioning models adopt autoregressive decoders, \ie they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they remove words dependence excessively. To make a better trade-off between speed and quality, we introduce a semi-autoregressive model for image captioning~(dubbed as SATIC), which keeps the autoregressive property in global but generates words parallelly in local. Based on Transformer, there are only a few modifications needed to implement SATIC. Extensive experiments on the MSCOCO image captioning benchmark show that SATIC can achieve a better trade-off without bells and whistles. Code is available at {\color{magenta}\url{https://github.com/YuanEZhou/satic}}.

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