3 items tagged with "Vision-Language Models"
MICCAI 2026 presentation on MIS-Ground, a factorial benchmark that isolates the language-side brittleness behind 3D medical spatial grounding, and MIS-SemSam, a training-free semantic-sampling decode rule that lifts Qwen3-VL-32B by 13.06% to 66.5% overall, the best open-weights result and above Gemini 3 Flash.
MICCAI 2026 poster: MIS-Ground measures 3D anatomical spatial grounding in vision-language models under a controlled factorial design, and MIS-SemSam fixes language-side brittleness at decode time for free.
MIS-Ground is a controlled factorial benchmark that isolates the language-side brittleness behind 3D medical spatial grounding in vision-language models. MIS-SemSam, a training-free semantic-sampling decode rule, lifts Qwen3-VL-32B by 13.06% to 66.5% overall, the best open-weights result and above Gemini 3 Flash.