The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain's adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multi-view incremental framework named MVIL aimed at emulating the brain's fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network's capacity for generalization. Experimental results on six benchmark datasets show MVIL's effectiveness over state-of-the-art methods.

翻译：多媒体技术的快速发展革新了人类感知方式，为多视角学习开辟了道路。然而，传统多视角学习方法专为固定数据视角场景设计，难以模拟人脑顺序处理信号的复杂认知过程。人脑结构通过精细的前馈与反馈机制无缝整合序列数据。相比之下，传统方法在处理跨域数据时泛化能力不足，这凸显了对模拟大脑适应性与动态整合能力创新策略的需求。本文提出一种受生物神经启发的多视角增量框架MVIL，旨在模拟大脑对顺序到达视角的细粒度融合。MVIL包含两个核心模块：结构化赫布可塑性与突触分区学习。结构化赫布可塑性通过重构权重结构来表达视角表征间的高度相关性，促进视角表征的细粒度融合。此外，突触分区学习通过抑制部分突触，能有效缓解权重的剧烈变化并保留旧知识。这些模块以仿生方式强化新获取信息与现有知识库间的关键关联，从而提升网络的泛化能力。在六个基准数据集上的实验结果表明，MVIL优于现有最先进方法。