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Transformers outshine convolutional neural networks and recurrent neural networks in many applications from various domains, including natural language processing, image classification and medical ...

Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model ...

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image ...

TransUNet, a Transformers-based U-Net framework, achieves state-of-the-art performance in medical image segmentation applications. U-Net, the U-shaped convolutional neural network architecture, ...

The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each window, have achieved outstanding success.