Keywords
vision AI
Demographic Surveys
sentiment Analysis
How to Cite
Abstract
The article begins by identifying the limitations of traditional survey methods, including inefficiencies and inaccuracies, and positions Deep Learning and Vision AI as transformative tools. Key applications highlighted include automated age, gender, and ethnicity estimation, as well as environmental and sentiment analysis.
References
1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
A foundational article on deep learning techniques, discussing architectures like convolutional neural networks (CNNs) and their applications.
2. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.
Introduces ResNet, a groundbreaking neural network architecture used in many Vision AI applications.
3. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.
Presents YOLO, a real-time object detection system widely used in Vision AI for demographic analysis.
4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Comprehensive book on deep learning covering theoretical foundations, architectures, and applications.
5. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Landmark paper on CNNs that revolutionized computer vision, including applications for demographic surveys.
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