  In this paper we introduce LifelongMemory, a new framework for accessing
long-form egocentric videographic memory through natural language question
answering and retrieval. LifelongMemory generates concise video activity
descriptions of the camera wearer and leverages the zero-shot capabilities of
pretrained large language models to perform reasoning over long-form video
context. Furthermore, Lifelong Memory uses a confidence and explanation module
to produce confident, high-quality, and interpretable answers. Our approach
achieves state-of-the-art performance on the EgoSchema benchmark for question
answering and is highly competitive on the natural language query (NLQ)
challenge of Ego4D. Code is available at
https://github.com/Agentic-Learning-AI-Lab/lifelong-memory.
