Na-IRSTD improves infrared small target detection by fusing native-resolution features with a selective token reduction strategy, reaching state-of-the-art results on four public benchmarks.
Hc-mamba: Vision mamba with hybrid convolutional techniques for medical image segmentation
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Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
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Na-IRSTD: Enhancing Infrared Small Target Detection via Native-Resolution Feature Selection and Fusion
Na-IRSTD improves infrared small target detection by fusing native-resolution features with a selective token reduction strategy, reaching state-of-the-art results on four public benchmarks.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.