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arXiv · 2506.16450

How Far Can Off-the-Shelf Multimodal Large Language Models Go in Online Episodic Memory Question Answering?

Giuseppe Lando·Rosario Forte·Giovanni Maria Farinella·Antonino Furnari
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Abstract

We investigate whether off-the-shelf Multimodal Large Language Models (MLLMs) can tackle Online Episodic-Memory Video Question Answering (OEM-VQA) without additional training. Our pipeline converts a streaming egocentric video into a lightweight textual memory, only a few kilobytes per minute, via an MLLM descriptor module, and answers multiple-choice questions by querying this memory with an LLM reasoner module. On the QAEgo4D-Closed benchmark, our best configuration attains 56.0% accuracy with 3.6 kB per minute storage, matching the performance of dedicated state-of-the-art systems while being 10**4/10**5 times more memory-efficient. Extensive ablations provides insights into the role of each component and design choice, and highlight directions of improvement for future research.

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Giuseppe Lando, Rosario Forte, Giovanni Maria Farinella, Antonino Furnari. 2025-06-19. How Far Can Off-the-Shelf Multimodal Large Language Models Go in Online Episodic Memory Question Answering?. https://arxiv.org/abs/2506.16450

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