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

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arxiv 2312.05269 v3 pith:L2QBZSOI submitted 2023-12-07 cs.CV cs.LG

Ying Wang , Yanlai Yang , Mengye Ren This is my paper · ORCID
classification cs.CVcs.LG
keywords lifelongmemoryansweringlanguagelong-formegocentricnaturalquestionvideo
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pro$^2$Assist: Continuous Step-aware Proactive Assistance with Multi-modal Egocentric Perception for Long-horizon Procedural Tasks

    cs.AI 2026-05 unverdicted novelty 7.0 of 10

    Pro²Assist uses multimodal egocentric perception from AR glasses to track fine-grained progress in long-horizon procedural tasks and deliver timely proactive assistance, outperforming baselines by over 21% in action u...

  2. ViSAGE: Constructing Self-Correcting Memories for Long-Form Video Understanding

    cs.AI 2026-07 conditional novelty 6.0 of 10

    ViSAGE builds entity-centered, self-correcting memories for long-form video understanding and reports state-of-the-art accuracy on M3-Bench and Video-MME-long.

  3. Native Active Perception as Reasoning for Omni-Modal Understanding

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    OmniAgent turns long-video understanding into a query-driven observe-think-act loop with a persistent text memory, outperforming larger passive models on LVBench.

  4. Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A 3B agent trained with supervised tool-use traces and reinforcement learning answers week-long egocentric video questions by dynamically selecting hierarchical retrieval, video-LLM, and VLM tools.

  5. HCQA-1.5 @ Ego4D EgoSchema Challenge 2025

    cs.CV 2025-05 conditional novelty 4.0 of 10

    An ensemble of LLMs with confidence filtering and low-confidence re-reasoning reaches 77% accuracy on the EgoSchema benchmark, up from 75% for the prior HCQA system.

  6. Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles

    cs.CV 2025-05 reject novelty 4.0 of 10

    A training-free ensemble of commercial VLMs with prompt and chain-of-thought engineering reaches 79% on EgoSchema, ranking 2nd in the CVPR 2025 challenge, but the ensemble weights are fit to the test labels.

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