2020 Computer Vision Breakthroughs: Self‑Supervised Learning, Transformer Attention Modeling, and Neural Radiance Fields
The talk reviews three major 2020 advances in computer vision—self‑supervised learning surpassing supervised pre‑training, the successful adoption of Transformer‑based attention models for detection and classification, and the emergence of Neural Radiance Fields for view synthesis—while highlighting related research from Microsoft Research Asia and the broader community.