Video sentiment analysis as a decision-making process is inherently complex, involving the fusion of decisions from multiple modalities and the so-caused cognitive biases. Inspired by recent advances in quantum cognition, we show that the sentiment judgment from one modality could be incompatible with the judgment from another, i.e., the order matters and they cannot be jointly measured to produce a final decision. Thus the cognitive process exhibits "quantum-like" biases that cannot be captured by classical probability theories. Accordingly, we propose a fundamentally new, quantum cognitively motivated fusion strategy for predicting sentiment judgments. In particular, we formulate utterances as quantum superposition states of positive and negative sentiment judgments, and uni-modal classifiers as mutually incompatible observables, on a complex-valued Hilbert space with positive-operator valued measures. Experiments on two benchmarking datasets illustrate that our model significantly outperforms various existing decision level and a range of state-of-the-art content-level fusion approaches. The results also show that the concept of incompatibility allows effective handling of all combination patterns, including those extreme cases that are wrongly predicted by all uni-modal classifiers.
翻译:作为决策过程的视频情绪分析本身就十分复杂,涉及多种模式和由来已久的认知偏差决定的融合。在量子认知的最新进展的启发下,我们表明,一种模式的情绪判断可能与另一种模式的判断不相容,即秩序事项,无法共同测量以作出最终决定。因此,认知过程显示出“量式”偏见,无法被古典概率理论所捕捉。因此,我们提出了一种预测情绪判断的根本性的新、量式认知驱动融合战略。特别是,我们以正负情绪判断和单式分类为量式的量式超集状态,将单式分类者作为相互不相容的可观察状态,在具有积极操作者评价措施的复杂价值的希尔伯特空间上作出。关于两个基准数据集的实验表明,我们的模型大大超越了现有各种决策级别和一系列最新内容级融合方法。结果还表明,不相容性概念允许有效处理所有组合模式,包括所有单式分类师错误预测的极端案例。