Generalizable Multi-Linear Attention Network

The majority of existing multimodal sequential learning methods focus on how to obtain powerful individual representations and neglect to effectively capture the multimodal joint representation. Bilinear attention network (BAN) is a commonly used integration method, which leverages tensor operations to associate the features of different modalities. However, BAN has a poor compatibility for more modalities, since the computational complexity of the attention map increases exponentially with the number of modalities. Based on this concern, we propose a new method called generalizable multi-linear attention network (MAN), which can associate more modalities in acceptable complexity with hierarchical approximation decomposition. Specifically, considering the fact that softmax attention kernels cannot be decomposed as linear operation directly, we adopt the addition random features mechanism to approximate the non-linear softmax functions with enough theoretical analysis. Furthermore, we also introduce the local sequential constraints, which can be combined with ARF conveniently, as positional information. We conduct extensive experiments on several datasets of corresponding tasks, the experimental results show that MAN could achieve competitive results compared with baseline methods, showcasing the effectiveness of our contributions.