The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.
@article{arxiv.2409.07736,
title = {Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities},
author = {Aaryan Panda and Damodar Panigrahi and Shaswata Mitra and Sudip Mittal and Shahram Rahimi},
journal= {arXiv preprint arXiv:2409.07736},
year = {2024}
}