Papers › Order-Embeddings of Images and Language
Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training | % Test Accuracy | 81.4 | #88 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training | % Train Accuracy | 98.8 | #88 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training | Parameters | 15m | #88 of 98 | Archive leaderboard | report |
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