A flexible testing environment for visual question answering with performance evaluation
| Autor: | Mihael Cudic, Ryan Burt, Eder Santana, Jose C. Principe |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Cluttering
business.industry Computer science Cognitive Neuroscience Context (language use) 02 engineering and technology 010501 environmental sciences medicine.disease Object (computer science) Machine learning computer.software_genre 01 natural sciences Computer Science Applications Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Question answering 020201 artificial intelligence & image processing Artificial intelligence business computer MNIST database 0105 earth and related environmental sciences |
| Zdroj: | Neurocomputing. 291:128-135 |
| ISSN: | 0925-2312 |
| Popis: | In order to move toward efficient autonomous learning, we must have control over our datasets to test and adaptively train systems for complex problems such as Visual Question Answering (VQA). Thus, we created a testing environment around MNIST images with optional cluttering. Although less complex than publicly available VQA datasets, the new environment generates datasets that decouple answers from questions and incorporate abstract ideas (content, context, and arithmetic) that must be learned. In addition, we analyze the performance of merged CNNs and LSTMs using the environment while exploring different ways to incorporate pretrained object classifiers. We demonstrate the usefulness of our environment as well as provide insight on the limitations of simple architectures and the complexities of different questions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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