LifelongMemory: Leveraging LLMs for Answering Queries in Long-form Egocentric Videos

• URL: http://arxiv.org/abs/2312.05269v3
• Date: Tue, 05 Nov 2024 22:08:14 GMT
• Title: LifelongMemory: Leveraging LLMs for Answering Queries in Long-form Egocentric Videos
• Authors: Ying Wang, Yanlai Yang, Mengye Ren,
• Abstract summary: LifelongMemory is a new framework for accessing long-form egocentric videographic memory through natural language question answering and retrieval. Our approach achieves state-of-the-art performance on the benchmark for question answering and is highly competitive on the natural language query (NLQ) challenge of Ego4D.
• Score: 15.127197238628396
• License:
• Abstract: 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, LifelongMemory 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.

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