商汤开源 SenseNova-Vision-7B-MoT，该模型将计算机视觉重构为多模态生成，无需任务专用头即可达到 SOTA，在分割与密集几何任务上超越 Google DeepMind 的 Vision Banana。具体对比：Referring segmentation（RefCOCOg cIoU）80.3 vs 73.8，Semantic segmentation（Cityscapes mIoU）71.2 vs 69.9，Depth estimation（NYUv2 δ1）98.1 vs 94.8，Surface normal（NYUv2 mean error）14.4 vs 17.8。模型支持语言或视觉提示引导，基于商汤十年 CV 积累。新任务由指令定义，而非新模型头。
𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗶𝗻𝗴 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮-𝗩𝗶𝘀𝗶𝗼𝗻-7𝗕-𝗠𝗼𝗧 — 𝗼𝗻𝗲 𝗺𝗼𝗱𝗲𝗹, 𝗮𝗹𝗹 𝗺𝗮𝗷𝗼𝗿 𝘃𝗶𝘀𝗶𝗼𝗻 𝘁𝗮𝘀𝗸𝘀.
Reimagines CV as multimodal generation. Steerable by language or visual prompts. 𝗡𝗼 𝘁𝗮𝘀𝗸-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗵𝗲𝗮𝗱𝘀 — 𝘀𝘁𝗶𝗹𝗹 𝗦𝗢𝗧𝗔, 𝘀𝘂𝗿𝗽𝗮𝘀𝘀𝗶𝗻𝗴 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱'𝘀 𝗩𝗶𝘀𝗶𝗼𝗻 𝗕𝗮𝗻𝗮𝗻𝗮 𝗼𝗻 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗱𝗲𝗻𝘀𝗲 𝗴𝗲𝗼𝗺𝗲𝘁𝗿𝘆.
📊 𝘃𝘀. 𝗩𝗶𝘀𝗶𝗼𝗻 𝗕𝗮𝗻𝗮𝗻𝗮 (𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱):
🔹Referring segmentation (RefCOCOg cIoU): 80.3 vs 73.8
🔹Semantic segmentation (Cityscapes mIoU): 71.2 vs 69.9
🔹Depth estimation (NYUv2 δ1): 98.1 vs 94.8
🔹Surface normal (NYUv2 mean error): 14.4 vs 17.8
Built on a decade of SenseTime's CV leadership — #1 in China's Vision AI market for 10 consecutive years, and named a Tech Innovator in global GenAI CV by Gartner. 𝗩𝗶𝘀𝗶𝗼𝗻, 𝗻𝗼𝘄 𝗻𝗮𝘁𝗶𝘃𝗲 𝘁𝗼 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀. New tasks, defined by instruction — not by new model heads.
来源：SenseTime · x.com