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Found 3 papers, 3 papers with code
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
An efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources.
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs
Our smallest model, GreedyViG-S, achieves 81. 1% top-1 accuracy on ImageNet-1K, 2. 9% higher than Vision GNN and 2. 2% higher than Vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters.
MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications
Our work proves that well designed hybrid CNN-GNN architectures can be a new avenue of exploration for designing models that are extremely fast and accurate on mobile devices.
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