Title:

A REVIEW OF DEEP LEARNING MODELS FOR IMAGE AND VIDEO RECOGNITION

Author:

Ms. Anita S. Raicar

Abstract:

Deep learning has revolutionized the field of computer vision by enabling highly accurate image and video recognition systems. With the rapid growth of data availability and computational power, deep learning models particularly convolutional and transformer-based architectures have demonstrated superior performance over traditional machine learning approaches. This review paper presents a comprehensive analysis of deep learning models used for image and video recognition, focusing on their architectural evolution, performance characteristics, strengths, and limitations. Key models such as Convolutional Neural Networks, Residual Networks, Vision Transformers, and spatiotemporal video models are discussed. The paper also highlights challenges such as computational complexity, data dependency, and real-time deployment issues. By synthesizing findings from existing literature, this review provides insights into current trends and future research directions in deep learning-based visual recognition systems.

Keyword:

Image Recognition, Video Recognition, Convolutional Neural Networks