IndEgo：工业场景及协作工作的人际助理数据集
IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants
AI总结：
IndEgo数据集旨在通过工业场景中的协作工作提升人机交互助理的多模态理解与错误检测能力。
摘要：
我们介绍了IndEgo，一个多模态的注视和非注视数据集，针对常见的工业任务，包括装配/拆卸、物流与组织、检查与维修、木工等。该数据集包含3,460个注视记录（约197小时），以及1,092个非注视记录（约97小时）。数据集的重点是协作工作，其中两名工人共同完成认知和身体强度较高的任务。注视记录包含丰富的多模态数据，并通过眼动、叙述、声音、运动等添加了上下文。我们提供了详细的标注（动作、摘要、错误标注、叙述）、元数据、处理输出（眼动、手部姿态、半密集点云）以及对过程性和非过程性任务理解、错误检测和基于推理的问题回答的基准测试。基线评估显示，该数据集对最先进的多模态模型构成了挑战。我们的数据集可在：https://huggingface.co/datasets/FraunhoferIPK/IndEgo 获取。
英文摘要：
We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering. Baseline evaluations for Mistake Detection, Question Answering and collaborative task understanding show that the dataset presents a challenge for the state-of-the-art multimodal models. Our dataset is available at: https://huggingface.co/datasets/FraunhoferIPK/IndEgo
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
