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CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Open accessAbstract
Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN’s components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction.
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“The CNN has found applications in NLP, such as language modeling and analysis, despite RNNs being deemed more appropriate for these purposes (Bhatt et al. 2021).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Bhatt et al. 2021
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A review of convolutional neural networks in computer vision
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. D. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10721-6 · Cited by 1,477
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Metadata
- Authors
- Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H.
- Affiliations
- Parul University (IN)
- Computer Science & Engineering, DEPSTAR, Changa, Gujarat 388421, India
- Symbiosis International University (IN)
- Sankalchand Patel University (IN)
- Linnaeus University (SE)
- Corresponding author
- Patel, Chirag
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-10-11
- Volume
- 10
- Issue
- 20
- Pages
- 2470
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3206824631
- Cited by
- 890
- References
- 57
- Citations by year
- 2026: 100 · 2025: 266 · 2024: 270 · 2023: 189 · 2022: 63 · 2021: 2
- FWCI
- 47.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- convolutional neural networks, convolutional neural network architecture, object recognition, image classification, image segmentation, attention-based CNN, channel attention, multipath networks, depthwise convolution, activation functions, regularization
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