Ego-Grounding for Personalized Question-Answering in Egocentric Videos

TL;DR

This paper introduces MyEgo, a new dataset and benchmark for evaluating multimodal large language models' ability to perform personalized question-answering in egocentric videos, highlighting current limitations and the importance of ego-grounding.

Contribution

It provides the first egocentric VideoQA dataset and comprehensive analysis revealing the challenges and limitations of current models in ego-grounded personalized reasoning.

Findings

Models perform significantly below human accuracy.

Explicit evidence improves model performance temporarily.

Scaling models and explicit reasoning do not consistently enhance results.

Abstract

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. Data and code are available at https://github.com/Ryougetsu3606/MyEgo