XCiT- Cross-Covariance Image Transformers - Facebook AI Machine Learning Research Paper Explained

Explore a detailed analysis of the XCiT (Cross-Covariance Image Transformer) architecture, a novel approach in computer vision that addresses the scalability issues of traditional transformers. Delve into the key concepts of Cross-Covariance Attention (XCA), which operates across feature channels rather than tokens, reducing computational complexity from quadratic to linear. Examine how XCiT combines the accuracy of conventional transformers with the efficiency of convolutional architectures, enabling processing of high-resolution images. Learn about the theoretical and engineering considerations behind this innovation, and review experimental results across various vision benchmarks, including image classification, object detection, and semantic segmentation. Gain insights into the potential impact of XCiT on the field of deep learning and its implications for future developments in computer vision.

- Intro & Overview
- Self-Attention vs Cross-Covariance Attention XCA
- Cross-Covariance Image Transformer XCiT Architecture
- Theoretical & Engineering considerations
- Experimental Results
- Comments & Conclusion