Grounded Question-Answering in Long Egocentric Videos

Shangzhe Di, Weidi Xie; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 12934-12943

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 open-sourced at https://github.com/Becomebright/GroundVQA.