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arXiv · 1902.05829

Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection

Nikolaos Gkanatsios·Vassilis Pitsikalis·Petros Koutras·Athanasia Zlatintsi·Petros Maragos
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Abstract

Detecting visual relationships, i.e. triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.

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Nikolaos Gkanatsios, Vassilis Pitsikalis, Petros Koutras, Athanasia Zlatintsi, Petros Maragos. 2019-02-15. Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection. https://arxiv.org/abs/1902.05829

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