Name
Abstract | ||
|---|---|---|
Multimodal representations of text and images have become popular in recent years. Text however has inherent ambiguities when describing visual scenes, leading to the recent development of datasets with detailed graphical descriptions in the form of scene graphs. We consider the task of joint representation of semantically precise scene graphs and images. We propose models for representing scene graphs and aligning them with images. We investigate methods based on bag-of-words, subpath representations, as well as neural networks. Our investigation proposes and contrasts several models which can address this task and highlights some unique challenges in both designing models and evaluation. |
Year | Venue | Field |
|---|---|---|
2017 | international conference on learning representations | Graph,Pattern recognition,Computer science,Contrast (statistics),Artificial intelligence,Artificial neural network |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eugene Belilovsky | 1 | 23 | 6.88 |
| Matthew B. Blaschko | 2 | 1149 | 71.66 |
| Kiros, Ryan | 3 | 2265 | 94.80 |
| Raquel Urtasun | 4 | 6810 | 304.97 |
| Richard S. Zemel | 5 | 4958 | 425.68 |