Enhanced Image Captioning using Bidirectional Long Short-Term Memory and Convolutional Neural Networks

Abstract

The utilization of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BLSTM) networks in image captioning has significantly enhanced the quality and relevance of generated captions. In this approach, a CNN serves as the encoder to extract meaningful features from the input image, capturing its visual information. These features are then fed into a BLSTM network, acting as the decoder, which processes the features in a bidirectional manner to generate descriptive and coherent captions by considering both past and future context. The model is trained on a dataset of images and corresponding captions, fine-tuning the parameters to optimize the accuracy of caption generation. This combined approach effectively leverages the strengths of CNNs and BLSTMs, resulting in more detailed and contextually appropriate descriptions for the given images.