A Comparative Study of Pre-trained CNNs and GRU-Based Attention for
  Image Caption Generation
Rashid Khan Bingding Huang Haseeb Hassan Asim Zaman Zhongfu Ye
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.