TransUNet is a hybrid CNN-Transformer architecture that outperforms prior U-Net and Transformer baselines on multi-organ and cardiac medical image segmentation tasks.
In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition
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MambaKick reuses pretrained HAR embeddings with Mamba temporal modeling to predict penalty kick direction, reaching 53.1% accuracy on three classes and 64.5% on two classes.
citing papers explorer
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
TransUNet is a hybrid CNN-Transformer architecture that outperforms prior U-Net and Transformer baselines on multi-organ and cardiac medical image segmentation tasks.
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MambaKick: Early Penalty Direction Prediction from HAR Embeddings
MambaKick reuses pretrained HAR embeddings with Mamba temporal modeling to predict penalty kick direction, reaching 53.1% accuracy on three classes and 64.5% on two classes.