Learning Shared Multimodal Embeddings with Unpaired Data
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In this paper, we propose a method to learn a joint multimodal embedding space. We compare the effect of various constraints using paired text and video data. Additionally, we propose a method to improve the joint embedding space using an adversarial formulation with unpaired text and video data. In addition to testing on publicly available datasets, we introduce a new, large-scale text/video dataset. We experimentally confirm that learning such a shared embedding space benefits three difficult tasks (i) zero-shot activity classification, (ii) unsupervised activity discovery, and (iii) unseen activity captioning.
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