Name
Abstract | ||
|---|---|---|
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCV.2019.00439 | 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) |
DocType | ISSN | Citations |
Conference | 1550-5499 | 4 |
PageRank | References | Authors |
0.41 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Furkan Biten | 1 | 9 | 2.18 |
| Ruben Tito | 2 | 10 | 2.18 |
| Andrés Mafla | 3 | 12 | 2.89 |
| Lluís Gómez i Bigorda | 4 | 6 | 2.48 |
| Marçal Rusiñol | 5 | 386 | 33.57 |
| C.V Jawahar | 6 | 61 | 10.95 |
| Ernest Valveny | 7 | 647 | 41.65 |
| Dimosthenis Karatzas | 8 | 406 | 38.13 |