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Image captioning is a challenging task involving generating a textualdescription for an image using computer vision and natural language processingtechniques. This paper proposes a deep neural framework for image captiongeneration using a GRU-based attention mechanism. Our approach employs multiplepre-trained convolutional neural networks as the encoder to extract featuresfrom the image and a GRU-based language model as the decoder to generatedescriptive sentences. To improve performance, we integrate the Bahdanauattention model with the GRU decoder to enable learning to focus on specificimage parts. We evaluate our approach using the MSCOCO and Flickr30k datasetsand show that it achieves competitive scores compared to state-of-the-artmethods. Our proposed framework can bridge the gap between computer vision andnatural language and can be extended to specific domains.

A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation