AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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GLNet adopts a dual-branch encoder that combines a CNN-based Local Detail Perception Branch with a Mamba-based Global Context Modeling Branch, enabling the joint extraction of fine-grained local features and long-range semantic representations.
A novel MLIR-PL model with Visual–Label Alignment and Attribute-Aware Prompt and Attribute-Aware Prompt, which sufficiently harnesses the capabilities of large-scale pre-trained vision-language models is proposed, achieving the best performance comprehensively.
This work proposes a bidirectional uncertainty-aware region learning strategy to fully utilize the precise supervision provided by labeled data and stabilize the training of unlabeled data in semi-supervised medical image segmentation.
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