Component Analysis for Visual Question Answering Architectures
| Autor: | Camila Kolling, Jonatas Wehrmann, Rodrigo C. Barros |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Machine Learning (cs.LG) Knowledge extraction Component (UML) 0202 electrical engineering electronic engineering information engineering Question answering Set (psychology) 0105 earth and related environmental sciences Computer Science - Computation and Language business.industry Visualization Task analysis 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) computer Feature learning Natural language processing Natural language |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9206679 |
| Popis: | Recent research advances in Computer Vision and Natural Language Processing have introduced novel tasks that are paving the way for solving AI-complete problems. One of those tasks is called Visual Question Answering (VQA). A VQA system must take an image and a free-form, open-ended natural language question about the image, and produce a natural language answer as the output. Such a task has drawn great attention from the scientific community, which generated a plethora of approaches that aim to improve the VQA predictive accuracy. Most of them comprise three major components: (i) independent representation learning of images and questions; (ii) feature fusion so the model can use information from both sources to answer visual questions; and (iii) the generation of the correct answer in natural language. With so many approaches being recently introduced, it became unclear the real contribution of each component for the ultimate performance of the model. The main goal of this paper is to provide a comprehensive analysis regarding the impact of each component in VQA models. Our extensive set of experiments cover both visual and textual elements, as well as the combination of these representations in form of fusion and attention mechanisms. Our major contribution is to identify core components for training VQA models so as to maximize their predictive performance. |
| Databáze: | OpenAIRE |
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