视觉大模型 / VLM

Automated Hazard Detection in Construction Sites Using Large Language and Vision-Language Models

利用大语言和视觉-语言模型进行施工工地自动危险检测

Islem Sahraoui

机构 * University of Houston Cullen College of Engineering Department of Civil and Environmental Engineering（德克萨斯大学休斯顿分校库伦工程学院土木与环境工程系） 纠错

专题命中 视觉定位与Grounding ：vision-language model(title,abstract)；分类 cs.AI

AI总结 本研究利用大语言和视觉-语言模型，通过分析文本和图像数据，提高施工工地的安全隐患检测效率。

Comments Master thesis, University of Houton

TubeRMC: Tube-conditioned Reconstruction with Mutual Constraints for Weakly-supervised Spatio-Temporal Video Grounding

TubeRMC: 基于互约束的管状重建用于弱监督空间-时间视频定位

Jinxuan Li, Yi Zhang, Jian-Fang Hu, Chaolei Tan, Tianming Liang, Beihao Xia

专题命中 视觉定位与Grounding ：grounding(title,abstract)；分类 cs.CV

AI总结 TubeRMC通过引入互约束的管状重建方法，在弱监督条件下提升空间-时间视频定位的准确性和一致性。

Comments Accepted to AAAI 2026

Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback

通过闭环状态反馈接地LLMs用于机器人任务规划

Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami

专题命中 视觉定位与Grounding ：grounding(title,abstract)

AI总结 本文提出BrainBody-LLM方法，通过闭环反馈机制提升LLMs在机器人任务规划中的鲁棒性，实现在复杂任务中的显著性能提升。

Comments Preprint version. Accepted full paper available here: https://advanced.onlinelibrary.wiley.com/doi/10.1002/adrr.202500072

Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence

Conan：基于多尺度视觉证据的逐步学习以像侦探一样推理

Kun Ouyang, Yuanxin Liu, Linli Yao, Yishuo Cai, Hao Zhou, Jie Zhou, Fandong Meng, Xu Sun

机构 * State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学） ； WeChat AI, Tencent Inc., China（微信AI，腾讯公司，中国） 纠错

专题命中 视觉定位与Grounding ：visual reasoning(abstract)；grounding(abstract)；multimodal large language model(abstract)；分类 cs.CV

AI总结 Conan通过多阶段渐进冷启动策略和AIR RLVR框架，实现证据基础的多步视频推理，超越基线模型，达到最先进的性能。

A First-Principles Based Risk Assessment Framework and the IEEE P3396 Standard

基于第一性原理的风险评估框架及IEEE P3396标准

Richard J. Tong, Marina Cortês, Jeanine A. DeFalco, Mark Underwood, Janusz Zalewski

机构 * Chair, IEEE Artificial Intelligence Standards Committee (AISC) ； Institute of Astrophysics ； Space Sciences, University of Lisbon, Portugal ； Vice Chair, IEEE Artificial Intelligence Standards Committee University of New Haven ； Florida Gulf Coast University, United States ； State Academy of Applied Sciences, Ciechanow, Poland 纠错

专题命中 视觉定位与Grounding ：grounding(abstract)；分类 cs.AI

AI总结 本文提出基于第一性原理的生成式AI风险评估框架，通过信息分类系统识别不同风险类型并归责于相关方，旨在提升AI治理的严谨性与责任性。

Comments 8 pages with 3 tables. This manuscript is prepared for publication by the Institute of Electrical and Electronics Engineers, Standards Association (IEEE-SA), Sponsor Committee - Artificial Intelligence Standards Committee (C/AISC) as a White Paper of Working Group p3396 at https://standards.ieee.org/ieee/3396/11379/

Journal ref 2025 IEEE Conference on Artificial Intelligence (CAI), pp. 1588-1595, 2025