Compositional Mixture Representations for Vision and Text
Stephan Alaniz, Marco Federici, Zeynep Akata
Workshop on Learning with Limited Labelled Data for Image and Video Understanding (L3D-IVU), CVPR
2022

Abstract

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.

(c) 2021 Explainable Machine Learning Tübingen Impressum