We propose the Multi-Head Gaussian Adaptive Attention Mechanism (GAAM), a
novel probabilistic attention framework, and the Gaussian Adaptive Transformer
(GAT), designed to enhance information aggregation across multiple modalities,
including Speech, Text and Vision. GAAM integrates learnable mean and variance
into its attention mechanism, implemented in a Multi-Headed framework enabling
it to collectively model any Probability Distribution for dynamic recalibration
of feature significance. This method demonstrates significant improvements,
especially with highly non-stationary data, surpassing the state-of-the-art
attention techniques in model performance (up to approximately +20% in
accuracy) by identifying key elements within the feature space. GAAM's
compatibility with dot-product-based attention models and relatively low number
of parameters showcases its adaptability and potential to boost existing
attention frameworks. Empirically, GAAM exhibits superior adaptability and
efficacy across a diverse range of tasks, including emotion recognition in
speech, image classification, and text classification, thereby establishing its
robustness and versatility in handling multi-modal data. Furthermore, we
introduce the Importance Factor (IF), a new learning-based metric that enhances
the explainability of models trained with GAAM-based methods. Overall, GAAM
represents an advancement towards development of better performing and more
explainable attention models across multiple modalities.

多头高斯自适应注意力机制（GAAM）和高斯自适应变换器（GAT）能够增强跨多种模态（包括语音、文本和视觉）的信息聚合，有效提升模型性能，特别是在处理高度非平稳数据时的准确度可以提升 20% 左右，并通过识别特征空间中的关键元素，进一步展示了模型的适应性和潜力。GAAM 不仅兼容基于点积的注意力模型，而且参数较少，展现了其对现有注意力框架的提升能力和潜在价值。通过实验证明，GAAM 在多种任务上具备出色的适应性和效果，包括情感识别、图像分类和文本分类，从而显示出其在处理多模态数据方面的健壮性和多功能性。此外，该研究还引入了重要性因子（IF），一种基于学习的度量指标，增强了 GAAM 方法训练模型的可解释性。总体而言，GAAM 代表了跨多模态的注意力模型在性能和可解释性方面的进展。