Jain, Jayant (2022) Explaining Model Predictions in VQA. Masters thesis, Indian Institute of Science Education and Research Kolkata.
![[img]](http://eprints.iiserkol.ac.in/style/images/fileicons/text.png) |
Text (MS dissertation of Jayant Jain (17MS075))
17MS075_Thesis_file.pdf - Submitted Version Restricted to Repository staff only Download (4MB) |
Abstract
Recent state-of-the-art VQA systems have shown great accuracy results. Many recent works suggest that many VQA models do not necessarily rely on image content while making predictions and can resort to learning superficial correlations from the structure of natural language. This makes the model biased. Moreover, they do not necessarily attend to image features that we humans consider necessary for answering a question. This raises the need to be able to explain such models’ predictions. This work explores the model given by Wu and Mooney [32] and attempts to explain this model’s predictions.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | Supervisor: Prof. Utpal Garain, Computer Vision Pattern Recognition (CVPR) Unit, Indian Statistical Institute, Kolkata; IISER Kolkata: Dr. Anirvan Chakraborty |
| Uncontrolled Keywords: | Counterfactual Analysis; Explainable Models; Language Priors; Visual Question Answering |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Department of Mathematics and Statistics |
| Depositing User: | IISER Kolkata Librarian |
| Date Deposited: | 07 Jun 2023 10:40 |
| Last Modified: | 07 Jun 2023 10:40 |
| URI: | http://eprints.iiserkol.ac.in/id/eprint/1300 |
Actions (login required)
 |
View Item |
