Core Insights - The article discusses the evolution of computer vision towards a new paradigm, emphasizing the transition from basic pixel perception to complex spatial reasoning and world modeling, facilitated by Test-time Scaling (TTS) [2][5] - The upcoming ViSCALE 2026 conference aims to gather leading scholars to explore breakthroughs in visual models through computational expansion, focusing on deep reasoning rather than mere static outputs [4][5] Group 1: Conference Highlights - ViSCALE 2026 will feature discussions on spatial intelligence and world models, with contributions from top scholars including Sergey Levine, Manling Li, and Ziwei Liu [5] - The conference encourages innovative research submissions that challenge existing visual model limitations, providing a platform for both theoretical and application-focused studies [7] Group 2: Key Topics of Discussion - The conference will cover various topics, including: - Enhancing video generation's physical consistency and long-term causal reasoning through TTS [6] - Breaking 2D limitations to enable models to navigate and operate in 3D spaces like humans [6] - Developing visual reasoning chains that allow models to self-correct and engage in multi-step reasoning [6] - Exploring scaling laws that relate computational load during testing to visual reasoning performance [6] Group 3: Submission Details - The conference invites submissions in two tracks: Full Papers (8 pages) and Extended Abstracts (up to 4 pages), with specific formatting requirements [9] - Important deadlines include submission by March 10, 2026, and notification of acceptance by March 18, 2026 [9]

>> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 思维链 (Chain-of-Thought, CoT) 是一种通过中间思考步骤增强大语言模型推理能力的方法。视觉思维链 (Visual Chain-of-Thought, VCoT) 将思维链从文 本模态扩展到图像模态，以图像作为中间思考步骤，被用来提升多模态大模型的思考能力。 (a)基于多模态融合的方法, (b)使用LLM/VLM提供指引的模块化方法, (c)带有语言推理能力的端到端多模态大模型方法, (d)我们的方法，引入视觉推理能 力，以目标的bounding box图像作为思考步骤。 VCoT-Grasp构建了一个端到端的基础模型，并引入视觉思维链来增强视觉理解能力。实际运行中，模型以目标物品的bounding box图像作为中间思考步 骤，首先预测目标的bounding box作为粗粒度位置，之后目标区域的图像被裁剪并输入模型用以提供细粒 ...