Summary of Exploring the Synergies Of Hybrid Cnns and Vits Architectures For Computer Vision: a Survey, by Haruna Yunusa et al.
Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey
by Haruna Yunusa, Shiyin Qin, Abdulrahman Hamman Adama Chukkol, Abdulganiyu Abdu Yusuf, Isah Bello, Adamu Lawan
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This comprehensive review delves into the emerging trend of combining Convolutional Neural Network (CNN) and Vision Transformers (ViT) architectures, revolutionizing computer vision (CV). By examining state-of-the-art hybrid CNN-ViT models, this survey highlights the synergies between these approaches. The review covers key topics such as background on vanilla CNNs and ViTs, systematic taxonomic classification of hybrid designs, comparative analysis of different hybrid architectures, challenges and future directions for hybrid models, and concluding remarks with recommendations. This study aims to provide a guiding resource, shedding light on the intricate dynamics between CNNs and ViTs, ultimately shaping the future of CV architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about combining two types of computer vision models, Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). By joining these two approaches, researchers have made some exciting breakthroughs in this field. The paper takes a close look at all the different ways that people have combined CNNs and ViTs to create new, hybrid models. It also compares how well these different models work on certain tasks and looks at what challenges they face and where they might go from here. Overall, this study aims to help us understand more about how these two approaches can be used together to make even better computer vision models. |
Keywords
* Artificial intelligence * Classification * Cnn * Neural network * Vit
