Journal Article
Computer Vision

A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions

Supriya V. Mahadevkar(Symbiosis International University), Bharti Khemani(Symbiosis International University), Shruti Patil(Symbiosis International University), Ketan Kotecha(Symbiosis International University), Deepali Vora(Symbiosis International University), Ajith Abraham(Machine Intelligence Research Labs), Lubna A. Gabralla(Princess Nourah bint Abdulrahman University)
January 1, 2022IEEE Access279 citations
PDFDOI

279

Citations

6

Influential Citations

IEEE Access

Venue

2022

Year

Abstract

Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. Machine learning is automating human assistance by training an algorithm on relevant data. Supervised, Unsupervised, and Reinforcement Learning are the three fundamental categories of machine learning techniques. In this paper, we have discussed the different learning styles used in the field of Computer vision, Deep Learning, Neural networks, and machine learning. Some of the most recent applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos. Some machine learning techniques frequently include zero-shot learning, active learning, contrastive learning, self-supervised learning, life-long learning, semi-supervised learning, ensemble learning, sequential learning, and multi-view learning used in computer vision until now. There is a lack of systematic reviews about all learning styles. This paper presents literature analysis of how different machine learning styles evolved in the field of Artificial Intelligence (AI) for computer vision. This research examines and evaluates machine learning applications in computer vision and future forecasting. This paper will be helpful for researchers working with learning styles as it gives a deep insight into future directions.

Analysis

Why This Paper Matters

This paper addresses a critical gap in the computer vision literature: the absence of a unified, systematic review covering all major machine learning styles. As the field rapidly evolves with new paradigms like self-supervised and zero-shot learning, researchers often lack a comprehensive overview to situate their work. By cataloging techniques from supervised learning to multi-view learning, the authors provide a roadmap that helps practitioners choose appropriate methods for tasks such as object detection and image classification.

The timing of this review is significant given the explosion of data and the shift from traditional data processing to machine learning. The paper's emphasis on future directions makes it particularly useful for early-career researchers and those looking to explore emerging areas like contrastive and life-long learning.

Technical Contributions

The paper's main technical contribution is its taxonomy and analysis of learning styles in computer vision:

  • Comprehensive coverage: Includes nine advanced techniques beyond the three fundamental categories: zero-shot, active, contrastive, self-supervised, life-long, semi-supervised, ensemble, sequential, and multi-view learning.
  • Application focus: Links each learning style to concrete computer vision tasks (object identification, classification, information extraction from images/videos).
  • Evolutionary perspective: Traces how learning styles have evolved within AI, providing historical context.
  • Future forecasting: Offers insights into promising research directions, which is rare in survey papers.

Results

As a review paper, the primary result is the structured synthesis of existing work. The paper does not present new experimental results or quantitative benchmarks. Instead, it highlights the lack of systematic reviews as a key finding, and its value lies in the organization and analysis of over 279 cited works. The authors successfully demonstrate that while supervised learning dominates, emerging styles like self-supervised and zero-shot learning are gaining traction.

Significance

This paper's broader impact is in providing a foundational reference for the computer vision community. By clarifying the landscape of learning styles, it can help researchers avoid redundant efforts and identify underexplored niches. For practitioners at Neura Market, this review serves as a decision-making tool: when building a vision system, one can quickly assess which learning style aligns with their data availability and task constraints. The paper also underscores the trend toward less supervised approaches, which is critical for real-world applications where labeled data is scarce.

computer-visionmachine-learningdeep-learningsupervised-learningunsupervised-learningreinforcement-learningself-supervised-learningzero-shot-learning
Reinforcement LearningComputer VisionNeural Networks

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