Routing with Self-Attention for Multimodal Capsule Networks

TL;DR

This paper introduces a scalable multimodal capsule network with a novel self-attention routing mechanism, enabling effective learning from large-scale noisy video data and improving multimodal task performance.

Contribution

The paper proposes a new self-attention based routing method for capsule networks, allowing large-scale multimodal learning with improved efficiency and robustness.

Findings

Outperforms traditional routing techniques in multimodal tasks

Scales effectively to large video datasets

Achieves competitive results on multiple benchmarks

Abstract

The task of multimodal learning has seen a growing interest recently as it allows for training neural architectures based on different modalities such as vision, text, and audio. One challenge in training such models is that they need to jointly learn semantic concepts and their relationships across different input representations. Capsule networks have been shown to perform well in context of capturing the relation between low-level input features and higher-level concepts. However, capsules have so far mainly been used only in small-scale fully supervised settings due to the resource demand of conventional routing algorithms. We present a new multimodal capsule network that allows us to leverage the strength of capsules in the context of a multimodal learning framework on large amounts of video data. To adapt the capsules to large-scale input data, we propose a novel routing by self-attention mechanism that selects relevant capsules which are then used to generate a final joint multimodal feature representation. This allows not only for robust training with noisy video data, but also to scale up the size of the capsule network compared to traditional routing methods while still being computationally efficient. We evaluate the proposed architecture by pretraining it on a large-scale multimodal video dataset and applying it on four datasets in two challenging downstream tasks. Results show that the proposed multimodal capsule network is not only able to improve results compared to other routing techniques, but also achieves competitive performance on the task of multimodal learning.