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- W2912220568 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." @default.
- W2912220568 created "2019-02-21" @default.
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- W2912220568 date "2019-02-15" @default.
- W2912220568 modified "2023-09-27" @default.
- W2912220568 title "Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection" @default.
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