  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. Code, data, and models are available
at https://github.com/Becomebright/GroundVQA.
