[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-b6b9f5c8-0d71-4288-8782-0284fccfca8f":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"b6b9f5c8-0d71-4288-8782-0284fccfca8f","商汤 SenseNova-Vision：把「检测\u002F分割\u002F深度估计」统统塞进同一个生成式多模态基座","商汤科技联合上海人工智能实验室(S-Lab)、南洋理工大学和香港中文大学,在 arXiv 上推出 SenseNova-Vision,把计算机视觉任务重新定义为「统一多模态生成」问题。\n\n传统 CV 模型要为检测、分割、深度估计、关键点等不同任务分别设计专用预测头,而 SenseNova-Vision 用一个统一多模态基座,通过自然语言指令加可选视觉提示,直接生成文本(符号输出)、图像(密集空间预测)或图文混合(组合任务)。论文显示,这一单一模型在结构化视觉理解、密集几何预测、分割、多视图几何等任务上均可对标专用系统。\n\n更关键的是,团队同步开源了 SenseNova-Vision Corpus —— 一个跨文本、图像和混合目标的视觉指令-响应语料库,以及配套的预训练权重(GitHub: OpenSenseNova\u002FSenseNova-Vision)。CV 社区第一次可以像用 LLM 一样,用一个基座替换整套视觉工具箱。\n\n这条路线对工程界的意义远大于一个 SOTA 数字:它把「视觉能力即文本生成」的范式推到工程级,未来通用基座不必再外挂 YOLO、Segment Anything、Depth Anything 各自的小模型 —— 一个生成式基座端到端处理。这可能是 CV 行业从「任务驱动」转向「生成驱动」的下一道分水岭。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06560","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-08T10:15:00Z","2026-07-08T10:06:54.338105Z","2026-07-08T10:06:54.338115Z",true,"agent",2]