arXiv:2504.10739v1 Announce Type: new
Abstract: Comprehending extended audiovisual experiences remains a fundamental challenge for computational systems. Current approaches struggle with temporal integration and cross-modal associations that humans accomplish effortlessly through hippocampal-cortical networks. We introduce HippoMM, a biologically-inspired architecture that transforms hippocampal mechanisms into computational advantages for multimodal understanding. HippoMM implements three key innovations: (i) hippocampus-inspired pattern separation and completion specifically designed for continuous audiovisual streams, (ii) short-to-long term memory consolidation that transforms perceptual details into semantic abstractions, and (iii) cross-modal associative retrieval pathways enabling modality-crossing queries. Unlike existing retrieval systems with static indexing schemes, HippoMM dynamically forms integrated episodic representations through adaptive temporal segmentation and dual-process memory encoding. Evaluations on our challenging HippoVlog benchmark demonstrate that HippoMM significantly outperforms state-of-the-art approaches (78.2% vs. 64.2% accuracy) while providing substantially faster response times (20.4s vs. 112.5s). Our results demonstrate that translating neuroscientific memory principles into computational architectures provides a promising foundation for next-generation multimodal understanding systems. The code and benchmark dataset are publicly available at https://github.com/linyueqian/HippoMM.
HippoMM: A Biologically-Inspired Architecture for Multimodal Understanding
In the field of multimedia information systems, the challenge of comprehending extended audiovisual experiences has always been a major concern. Humans effortlessly integrate audio and visual information and make cross-modal associations through their hippocampal-cortical networks, which is a complex cognitive process. However, current computational systems struggle with this task.
In a recent study, researchers have introduced a novel architecture called HippoMM, which takes inspiration from the hippocampus, a brain region known for its role in memory formation and spatial navigation.
Key Innovations of HippoMM
- Hippocampus-inspired pattern separation and completion: HippoMM leverages the pattern separation and completion mechanisms observed in the hippocampus. This allows it to handle continuous audiovisual streams effectively.
- Short-to-long term memory consolidation: HippoMM converts perceptual details into semantic abstractions by consolidating them from short-term memory to long-term memory. This helps in transforming raw sensory information into meaningful representations.
- Cross-modal associative retrieval pathways: HippoMM facilitates modality-crossing queries by creating cross-modal associative retrieval pathways. This enables the system to provide integrated and contextually relevant responses.
Unlike existing retrieval systems that use static indexing schemes, HippoMM dynamically forms episodic representations by adapting temporal segmentation and dual-process memory encoding. This enables it to create a more cohesive and accurate understanding of multimodal content.
Implications and Future Perspectives
HippoMM demonstrates the potential of translating neuroscientific memory principles into computational architectures for advancing multimodal understanding systems. By incorporating hippocampal mechanisms, the system achieves a significantly higher accuracy of 78.2% compared to the state-of-the-art approaches that achieve 64.2% accuracy. Moreover, HippoMM also exhibits faster response times, taking only 20.4 seconds compared to 112.5 seconds with existing methods.
The interdisciplinary nature of this research is evident in the fusion of neuroscience, information systems, and artificial intelligence. By bridging these fields, HippoMM opens up new possibilities for applications in areas such as virtual reality, augmented reality, and artificial reality. The ability to comprehend and integrate audiovisual experiences is crucial in these domains, and HippoMM’s approach can significantly enhance the user experience and interaction.
The availability of the HippoVlog benchmark dataset and code on GitHub further promotes reproducibility and encourages researchers to build upon this work. It also enables the benchmarking of future multimodal understanding systems against the performance of HippoMM.
In conclusion, HippoMM represents a promising step towards developing next-generation multimodal understanding systems by leveraging the insights from neuroscience and computational modeling. The integration of audio and visual information through a biologically-inspired architecture brings us closer to bridging the gap between human-like understanding and computational systems.
References:
The original research paper and code can be accessed at:
https://arxiv.org/abs/2504.10739v1
The HippoMM benchmark dataset and code can be found on GitHub:
https://github.com/linyueqian/HippoMM