A Position-Aware Transformer for Image Captioning
A Position-Aware Transformer for Image Captioning
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DOI：
10.32604/cmc.2022.019328
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Zelin Deng;Bo Zhou;Pei He;Jian Huang;O. Alfarraj;Amr M. Tolba
中科院分区：
文献类型：
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作者：
Zelin Deng;Bo Zhou;Pei He;Jian Huang;O. Alfarraj;Amr M. Tolba
: Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features. In this work, we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above problems. The image-feature attention firstly extracts multi-level features by using Feature Pyramid Network (FPN), then utilizes the scaled-dot-product to fuse these features, which enables our model to detect objects of different scales in the image more effectively without increasing parameters. In the position-aware attention mechanism, the relative positions between image features are obtained at first, afterwards the relative positions are incorporated into the originalimage features to generate captions more accurately. Experiments are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4, METEOR, ROUGE-L, CIDEr scores compared with some state-of-the-art approaches, demonstrating the effectiveness of our approach.
