Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2015 | international conference on learning representations | Closed captioning,Textual entailment,Computer science,Artificial intelligence,Natural language processing,Hierarchy,Machine learning |
DocType | Volume | Citations |
Journal | abs/1511.06361 | 73 |
PageRank | References | Authors |
2.28 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ivan vendrov | 1 | 73 | 2.62 |
| Kiros, Ryan | 2 | 2265 | 94.80 |
| Sanja Fidler | 3 | 183 | 10.30 |
| Raquel Urtasun | 4 | 6810 | 304.97 |