Mask Transformer

Mask Transformers are a class of deep learning models that leverage the attention mechanism of transformers to perform dense prediction tasks, particularly image segmentation, by predicting labels for entire masks rather than individual pixels. Current research focuses on improving the accuracy and efficiency of these models across diverse applications, including medical image analysis, autonomous driving, and remote sensing, through techniques like incorporating confidence estimations, adaptive masking strategies, and multi-modal data fusion. This approach offers significant advantages in handling complex scenes with occlusions and variations in object appearance, leading to improved performance in various fields compared to traditional methods.