Abstract
The Transformer architecture represents state-of-the-art in image captioning tasks. However, even the transformer uses positional encodings to encode sentences, its performance still not good enough in grammar. To improve the performance of image captioning, we present Prior Language Knowledge Transformer (PLKT)—a transformer-based model that can integrate learned a priori language knowledge for image captioning. In our proposal, when our model predicts the next word, it not only depends on the previously generated sequence but also relies on prior language knowledge. To obtain prior language knowledge, we embed a learnable memory vector inside the self-attention. Meanwhile, we use reinforcement learning to fine-tune the model in training. To prove the advancement and promising effectiveness of PLKT, we compare our approach with other recent image captioning methods in the experiments. Through objective results, our proposal increased the CIDEr score of the baseline by 0.6 points on the “Karpathy” test split when tested on COCO2014 dataset. In subjective results, our approach generated sentences is obviously better than baseline in grammar.
D. Yan and W. Yu—These authors have contributed equally to this work.
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Acknowledgment
This research is supported by Sichuan Science and Technology Program (No. 2020YFS0307, No. 2020YFG0430, No. 2019YFS0146), Mianyang Science and Technology Program (2020YFZJ016).
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Yan, D., Yu, W., Zhang, Z., Gong, J. (2021). Transformer with Prior Language Knowledge for Image Captioning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_4
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