Papers by Maryam Ziaeefard
Towards Knowledge-Augmented Visual Question Answering (2020.coling-main)
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| Challenge: | Visual Question Answering (VQA) is a challenging task for humans, but it is effortless for visual-based approaches. |
| Approach: | They propose a visual-based approach that captures interactions between visual scenes and external knowledge sources and exploits ConceptNet as the source of general knowledge. |
| Outcome: | The proposed model learns a question-adaptive graph representation of related knowledge instances. |
ConceptBert: Concept-Aware Representation for Visual Question Answering (2020.findings-emnlp)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. |
| Approach: | They propose an algorithm which learns a joint Concept-Vision-Language embedding for questions which require common sense knowledge from external structured content. |
| Outcome: | The proposed model is based on the Outer Knowledge-VQA and VQA datasets. |