Rashid Khan

Rashid Khan, M Shujah Islam, Khadija Kanwal et al.

Image captioning is a fast-growing research field of computer vision and natural language processing that involves creating text explanations for images. This study aims to develop a system that uses a pre-trained convolutional neural network (CNN) to extract features from an image, integrates the features with an attention mechanism, and creates captions using a recurrent neural network (RNN). To encode an image into a feature vector as graphical attributes, we employed multiple pre-trained convolutional neural networks. Following that, a language model known as GRU is chosen as the decoder to construct the descriptive sentence. In order to increase performance, we merge the Bahdanau attention model with GRU to allow learning to be focused on a specific portion of the image. On the MSCOCO dataset, the experimental results achieve competitive performance against state-of-the-art approaches.

Rashid Khan, Bingding Huang, Haseeb Hassan et al.

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.