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Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities

arXiv 2409.07736 ↗First posted 12 Sep 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 61 references that are in the map. First posted on arXiv 12 Sep 2024.

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17 of 61 references.

References · 17

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5 more
  1. 2024LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledgecited by 22
  2. 2018Autofocus Layer for Semantic Segmentationcited by 13
  3. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  4. 2024A Survey on Privacy Attacks Against Digital Twin Systems in AI-Roboticscited by 3
  5. 2023URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environmentscited by 2

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