Image captioning is a challenging task involving generating a textual
description for an image using computer vision and natural language processing
techniques. This paper proposes a deep neural framework for image caption
generation using a GRU-based attention mechanism. Our approach employs multiple
pre-trained convolutional neural networks as the encoder to extract features
from the image and a GRU-based language model as the decoder to generate
descriptive sentences. To improve performance, we integrate the Bahdanau
attention model with the GRU decoder to enable learning to focus on specific
image parts. We evaluate our approach using the MSCOCO and Flickr30k datasets
and show that it achieves competitive scores compared to state-of-the-art
methods. Our proposed framework can bridge the gap between computer vision and
natural language and can be extended to specific domains.

使用深度神经网络和 GRU 注意机制对图像进行描述生成的研究提出了一种深度神经网络框架，通过多个预训练的卷积神经网络作为编码器从图像中提取特征，并使用 GRU 语言模型作为解码器生成描述性句子。通过将 Bahdanau 注意模型与 GRU 解码器集成，我们的方法提高了性能，并在 MSCOCO 和 Flickr30k 数据集上展示出与最先进方法相比具有优势的分数。我们提出的框架可以弥合计算机视觉和自然语言之间的差距，并可以扩展到特定领域。