Name
Abstract | ||
|---|---|---|
We present a holistic data-driven approach to image description generation, exploiting the vast amount of (noisy) parallel image data and associated natural language descriptions available on the web. More specifically, given a query image, we retrieve existing human-composed phrases used to describe visually similar images, then selectively combine those phrases to generate a novel description for the query image. We cast the generation process as constraint optimization problems, collectively incorporating multiple interconnected aspects of language composition for content planning, surface realization and discourse structure. Evaluation by human annotators indicates that our final system generates more semantically correct and linguistically appealing descriptions than two nontrivial baselines. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | constraint optimization problem,natural language description,language composition,generation process,similar image,novel description,query image,collective generation,parallel image data,natural image description,linguistically appealing description,image description generation |
Field | DocType | Volume |
Constraint optimization problem,Image description,Computer science,Natural language,Natural language processing,Artificial intelligence,Machine learning,Discourse structure | Conference | P12-1 |
Citations | PageRank | References |
117 | 6.35 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Polina Kuznetsova | 1 | 293 | 15.86 |
| Vicente Ordonez | 2 | 1418 | 69.65 |
| Alexander C. Berg | 3 | 10554 | 630.24 |
| Tamara L. Berg | 4 | 3221 | 225.32 |
| Yejin Choi | 5 | 2239 | 153.18 |