Image Captioning with Attention for Smart Local Tourism using EfficientNet

TL;DR

This paper presents a tourism-specific image captioning model using EfficientNet and attention mechanisms, improving descriptive accuracy for local tourism images to support AI-assisted tourism applications.

Contribution

Introduces a novel image captioning model tailored for local tourism, utilizing EfficientNet and attention, with a new dataset and comparison of architectures.

Findings

EfficientNetB0 achieved BLEU scores of 73.39 (training) and 24.51 (validation).

The model generates logical, human-like captions for tourism images.

EfficientNet-based models outperform VGG16 and InceptionV3 in this task.

Abstract

Smart systems have been massively developed to help humans in various tasks. Deep Learning technologies push even further in creating accurate assistant systems due to the explosion of data lakes. One of the smart system tasks is to disseminate users needed information. This is crucial in the tourism sector to promote local tourism destinations. In this research, we design a model of local tourism specific image captioning, which later will support the development of AI-powered systems that assist various users. The model is developed using a visual Attention mechanism and uses the state-of-the-art feature extractor architecture EfficientNet. A local tourism dataset is collected and is used in the research, along with two different kinds of captions. Captions that describe the image literally and captions that represent human logical responses when seeing the image. This is done to make the captioning model more humane when implemented in the assistance system. We compared the performance of two different models using EfficientNet architectures (B0 and B4) with other well known VGG16 and InceptionV3. The best BLEU scores we get are 73.39 and 24.51 for the training set and the validation set respectively, using EfficientNetB0. The captioning result using the developed model shows that the model can produce logical caption for local tourism-related images