AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes UltraG-Agent, which combines the semantic reasoning capabilities of a VLM with the ultrasound-specific segmentation capability of UltraSAM3, and shows that UltraG-Agent substantially improves both semantic prediction and pixel-level visual grounding.
This work proposes MedVCoT, which incorporates latent visual reasoning into the medical visual question answering (VQA) domain, and utilizes the specialized expertise of MedSAM to train a large vision-language model so that it can autonomously generate consistent and continuous latent visual tokens within Visual Chain-...
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