The current state-of-the-art in image segmentation includes models like Mask R-CNN, DeepLabV3+, and Vision Transformers (ViTs) for segmentation. These models leverage advanced architectures, such as attention mechanisms and atrous convolutions, to achieve high accuracy on benchmark datasets like COCO and Pascal VOC. Vision Transformers have gained prominence for their ability to capture global context and handle large-scale datasets. Research continues to improve segmentation models in terms of accuracy, efficiency, and generalizability.
Which is the current state of the art in image segmentation?
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