Efficient Large-Scale Multi-Modal Classification

Conference on Artificial Intelligence (AAAI)


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.

Related Publications

All Publications

EMNLP - October 31, 2021

Evaluation Paradigms in Question Answering

Pedro Rodriguez, Jordan Boyd-Graber

ASRU - December 13, 2021

Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems

Yangyang Xia, Buye Xu, Anurag Kumar

IROS - September 1, 2021

Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation

Naoki Yokoyama, Sehoon Ha, Dhruv Batra

EMNLP - November 16, 2020

Abusive Language Detection using Syntactic Dependency Graphs

Kanika Narang, Chris Brew

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy