Efficient Large-Scale Multi-Modal Classification

While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network. In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly. We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency. Our findings indicate that the inclusion of continuous information improves performance over text-only on a range of multi-modal classification tasks, even with simple fusion methods. In addition, we experiment with discretizing the continuous features in order to speed up and simplify the fusion process even further. Our results show that fusion with discretized features outperforms text-only classification, at a fraction of the computational cost of full multimodal fusion, with the additional benefit of improved interpretability. Text classification is one of the core problems in machine learning and natural language processing (Borko and Bernick 1963; Sebastiani 2002). It plays a crucial role in important tasks ranging from document retrieval and categorization to sentiment and topic classification (Deerwester et al. 1990; Joachims 1998; Pang and Lee 2008). However, while the incipient Web was largely text-based, the recent decade has seen a surge in multi-modal content: billions of images and videos are posted and shared online every single day. That is, text is either replaced as the dominant modality, as is the case with Instagram posts or YouTube videos, or it is augmented with non-textual content, as with most of today’s web pages. This makes multi-modal classification an important problem. Here, we examine the task of multi-modal classification using neural networks. We are primarily interested in two questions: what is the best way to combine (i.e., fuse) data from different modalities, and how can we do so in the most efficient manner? We examine various efficient multi-modal fusion methods and investigate ways to speed up the fusion process. In particular, we explore discretizing the continuous features, which leads to much faster training and requires  To the best of our knowledge, this work constitutes the first attempt to examine the accuracy/speed trade-off in multi-modal classification; and the first to directly show the value of discretized features in this particular task. If current trends continue, the Web will become increasingly multi-modal, making the question of multi-modal classification ever more pertinent. At the same time, as the Web keeps growing, we have to be able to efficiently handle ever larger quantities of data, making it important to focus on machine learning methods that can be applied to large-scale scenarios. This work aims to examine these two questions together. Our contributions are as follows. First, we compare various multi-modal fusion methods, examine their trade-offs, and show that simpler models are often desirable. Second, we experiment with discretizing continuous features in order to speed up and simplify the fusion process even further. Third, we examine learned representations for discretized features and show that they yield interpretability as a beneficial side effect. The work reported here constitutes a solid and scalable baseline for other approaches to follow; our investigation of discretized features shows how multi-modal classification does not necessarily imply a large performance penalty and is feasible in large-scale scenarios.