Multi Class Token Transformer

Multi-class token transformers are a novel approach leveraging the power of transformer networks to improve various computer vision tasks by incorporating multiple class tokens. Current research focuses on enhancing model architectures like MCTformer, improving the generation of class-specific localization maps for weakly supervised semantic segmentation, and applying these techniques to diverse applications such as autonomous driving and historical document image enhancement. This approach shows promise in improving sample efficiency and generalization capabilities in various domains, leading to more accurate and robust results compared to traditional methods.