The inspection of power transmission line has achieved notable achievements in the past few years, primarily due to the integration of deep learning technology. However, current inspection approaches continue to encounter difficulties in generalization and intelligence, which restricts their further applicability. In this paper, we introduce Power-LLaVA, the first large language and vision assistant designed to offer professional and reliable inspection services for power transmission line by engaging in dialogues with humans. Moreover, we also construct a large-scale and high-quality dataset specialized for the inspection task. By employing a two-stage training strategy on the constructed dataset, Power-LLaVA demonstrates exceptional performance at a comparatively low training cost. Extensive experiments further prove the great capabilities of Power-LLaVA within the realm of power transmission line inspection. Code shall be released.
Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases
China Three Gorges University（三峡大学）；College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；University of Birmingham（伯明翰大学）；Southern University of Science and Technology（南方科技大学）
ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；School of Engineering, Westlake University（西湖大学工程学院）；University of Minnesota – Twin Cities（明尼苏达大学双城分校）；University of Toronto（多伦多大学）
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound
ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC学院）；University of Washington（华盛顿大学）；Zhejiang University School of Medicine Sir Run Run Shaw Hospital（浙江大学医学院附属邵逸夫医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants
Fraunhofer IPK（弗劳恩霍夫研究所）；Technical University of Berlin（柏林技术大学）；University of Tübingen（图宾根大学）；RWTH Aachen University（亚琛工业大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
Department of Computer Science and Engineering, Southern University of Science and Technology（计算机科学与工程系，南方科技大学）；Macau University of Science and Technology（澳门科技大学）；Tencent YouTu Lab（腾讯YouTu实验室）；Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）；Centre for Frontier AI Research, A*STAR（前沿人工智能研究中心，A*STAR）
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Computing, Clemson University（克莱姆森大学计算机学院）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；The Keck School of Medicine of the University of Southern California（南加州大学凯克医学院）；Mayo Clinic（梅奥诊所）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
Khalifa University of Science and Technology（哈利法科技大学）；Abu Dhabi University（阿布扎比大学）；University of Bonn（波恩大学）；Lamarr Institute for ML and AI（拉马尔机器学习与人工智能研究所）；The University of Western Australia（西澳大学）
