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arXiv · 1904.05521

UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations

Hao Wu·Jiayuan Mao·Yufeng Zhang·Yuning Jiang·Lei Li·Weiwei Sun·Wei-Ying Ma
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Abstract

We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts. The space unifies the concepts at different levels, including objects, attributes, relations, and full scenes. A contrastive learning approach is proposed for the fine-grained alignment from only image-caption pairs. Moreover, we present an effective approach for enforcing the coverage of semantic components that appear in the sentence. We demonstrate the robustness of Unified VSE in defending text-domain adversarial attacks on cross-modal retrieval tasks. Such robustness also empowers the use of visual cues to resolve word dependencies in novel sentences.

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Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, Wei-Ying Ma. 2019-04-28. UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations. https://arxiv.org/abs/1904.05521

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