Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

中文翻译：

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通过基于模糊注意力的 DenseNet-BiLSTM 进行中文图像描述

中文图像描述生成任务通常存在一些挑战，例如单一特征提取、缺乏全局信息、缺乏对图像内容的详细描述。为了解决这些限制，我们在本文中提出了一种基于模糊注意力的 DenseNet-BiLSTM 中文图像字幕方法。在所提出的方法中，我们首先改进了密集连接网络以提取不同尺度的图像特征，并增强模型捕获弱特征的能力。同时，使用双向 LSTM 作为解码器，以增强对上下文信息的使用。引入改进的模糊注意机制有效地改善了图像特征与上下文信息的对应问题。我们在 AI Challenger 数据集上进行实验以评估模型的性能。结果表明，与其他模型相比，我们提出的模型在包括BLEU在内的客观量化评价指标上取得了更高的分数。 , 蓝 、 METEOR、ROUGEL 和苹果酒。生成的描述句可以准确地表达图像内容。