Investigating the Impact of Data Augmentation Techniques on Deep Learning-Based Image Captioning
Ms. Nidhi Ruhil · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: The rapid advancements in deep learning have transformed the landscape of computer vision, facilitating significant progress in tasks such as image classification, object detection, and image captioning. Image captioning, in particular, has emerged as a crucial application with wide-ranging implications across various domains, including accessibility, content understanding, and human-computer interaction. However, the effectiveness of deep learning models in image captioning tasks is contingent upon the availability and quality of annotated training data. Data augmentation techniques offer a promising avenue for addressing this challenge by artificially enriching the training datasets, thereby enhancing model robustness and generalization. This research paper embarks on a comprehensive investigation into the impact of data augmentation techniques on deep learning-based image captioning systems.