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The interaction between selection and aggregation in sparse attention networks can lead to super-resolution gains that are greater than the sum of their parts, challenging conventional wisdom in model design.
MambaPSA achieves a remarkable 17.6% boost in CPU inference speed while maintaining competitive accuracy in YOLO26, showcasing the potential of state space models in object detection.
Reload-Mamba achieves a remarkable 2.2 mIoU improvement over previous models by specifically addressing response dilution in multi-class semantic segmentation.
