Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation; (ii) employing large language models for efficient and scalable data synthesis; and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. We plan to publicly release the codes, model, and constructed datasets for future research.

翻译：现有视频理解方法主要针对第三人称视角的短视频设计，在机器人等领域的适用性存在局限。本文深入探索长时自我中心视频中的开放式问答（QA），使个体或机器人能够查询自身过去的视觉体验。该任务面临独特挑战：在大量视频内容中对查询进行时间维度的接地、精确数据标注的高资源需求，以及开放式答案因固有模糊性导致的评估困难。我们提出的方法通过以下方式应对这些挑战：（i）将查询接地与答案生成整合至统一模型以减少误差传播；（ii）利用大语言模型实现高效可扩展的数据合成；（iii）引入封闭式问答任务进行性能评估以管理答案歧义。大量实验证明了本方法的有效性，并在QAEgo4D和Ego4D-NLQ基准测试中达到最优性能。我们计划公开发布代码、模型及构建的数据集以支持后续研究。