面向第一人称视频个性化问答的自我指代 grounding
Ego-Grounding for Personalized Question-Answering in Egocentric Videos
为第一人称视觉语言模型提供了首个系统性的个性化问答基准 MyEgo，并揭示显式提供证据时模型短期提升、随时间衰减的现象，可直接用于设计带长程记忆与自我指代（ego-grounding）机制的第一人称视频问答模型与评测协议。
原文依据 We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文首次系统分析了多模态大语言模型（MLLMs）在需要自我指代 grounding 的个性化问答中的表现——即理解第一人称视频中拍摄者本人的能力。为此，我们提出 MyEgo，这是首个旨在评估 MLLMs 理解、记忆与推理拍摄者本人能力的第二人称视频问答数据集。MyEgo 包含 541 段长视频和 5K 个个性化问题，问题涉及“我的物品”“我的活动”和“我的过去”。基准测试表明，各类竞品 MLLMs——包括开源与闭源、思考型与非思考型、小规模与大规模——在 MyEgo 上均表现不佳。顶尖闭源与开源模型（如 GPT-5 和 Qwen3-VL）仅分别达到 46% 和 36% 的准确率，落后人类表现近 40% 和 50%。令人意外的是，显式推理与模型规模扩展均未带来一致的提升。当显式提供相关证据时模型表现有所改善，但增益随时间衰减，表明其在跟踪与记忆“我”和“我的过去”方面存在局限。这些发现共同凸显了自我指代 grounding 与长程记忆在实现第一人称视频个性化问答中的关键作用。我们希望 MyEgo 及我们的分析能推动第一人称个性化辅助在这些领域的进一步进展。
英文原文摘要 1334 字符
We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding - the ability to understand the camera-wearer in egocentric videos. To this end, we introduce MyEgo, the first egocentric VideoQA dataset designed to evaluate MLLMs' ability to understand, remember, and reason about the camera wearer. MyEgo comprises 541 long videos and 5K personalized questions asking about "my things", "my activities", and "my past". Benchmarking reveals that competitive MLLMs across variants, including open-source vs. proprietary, thinking vs. non-thinking, small vs. large scales all struggle on MyEgo. Top closed- and open-source models (e.g., GPT-5 and Qwen3-VL) achieve only 46% and 36% accuracy, trailing human performance by near 40% and 50% respectively. Surprisingly, neither explicit reasoning nor model scaling yield consistent improvements. Models improve when relevant evidence is explicitly provided, but gains drop over time, indicating limitations in tracking and remembering "me" and "my past". These findings collectively highlight the crucial role of ego-grounding and long-range memory in enabling personalized QA in egocentric videos. We hope MyEgo and our analyses catalyze further progress in these areas for egocentric personalized assistance.