Papers › HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding

HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding

14 Apr 2025arXiv:2504.10739links table onlyarchive 2025-07-28

Yueqian Lin, Jingyang Zhang, Qinsi Wang, Hancheng Ye, Yuzhe Fu, Yudong Liu, Hai "Helen" Li, Yiran Chen

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Comprehending extended audiovisual experiences remains challenging for computational systems, particularly temporal integration and cross-modal associations fundamental to human episodic memory. We introduce HippoMM, a computational cognitive architecture that maps hippocampal mechanisms to solve these challenges. Rather than relying on scaling or architectural sophistication, HippoMM implements three integrated components: (i) Episodic Segmentation detects audiovisual input changes to split videos into discrete episodes, mirroring dentate gyrus pattern separation; (ii) Memory Consolidation compresses episodes into summaries with key features preserved, analogous to hippocampal memory formation; and (iii) Hierarchical Memory Retrieval first searches semantic summaries, then escalates via temporal window expansion around seed segments for cross-modal queries, mimicking CA3 pattern completion. These components jointly create an integrated system exceeding the sum of its parts. On our HippoVlog benchmark testing associative memory, HippoMM achieves state-of-the-art 78.2% accuracy while operating 5x faster than retrieval-augmented baselines. Our results demonstrate that cognitive architectures provide blueprints for next-generation multimodal understanding. The code and benchmark dataset are publicly available at https://github.com/linyueqian/HippoMM.

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