THE ROLE OF DEEP LEARNING ARCHITECTURES IN COMPUTER VISION AND NATURAL LANGUAGE PROCESSING: A COMPREHENSIVE REVIEW

Abstract

The invention of powerful neural architectures has transformed the areas of computer vision and natural language processing with deep learning. This review addresses the history and use of the most well-known deep learning models, such as convolutional neural networks, recurrent neural networks, and transformers, in both CV and NLP fields. CNN-based models have proved to be incredibly powerful in image classification, object identification and segmentation in computer vision. Transformer-based models like BERT and GPT have surpassed conventional RNNs in NLP by allowing a scalable context modeling due to the self-attention mechanisms. Along with these achievements, deep learning systems have a number of limitations, namely high computational complexity, sensitivity to data, poor explainability, and susceptibility to bias and adversarial attacks. In this paper, some important benchmark datasets and evaluation measures to compare architectures will be discussed, and a critical evaluation of the existing shortcomings and future research avenues will be provided. Most of these challenges should be addressed in the future with respect to efficient architectures, explainability and multimodal learning. Through the synthesis of recent developments, comparative analysis, and implications, this review gives a thorough picture of how deep learning architectures are still defining the field of computer vision and natural language processing.