Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)
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| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
| Approach: | They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets. |
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Soravit Changpinyo, Linting Xue, Michal Yarom, Ashish Thapliyal, Idan Szpektor, Julien Amelot, Xi Chen, Radu Soricut
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| Challenge: | Existing benchmarking datasets for visual question answering focus on machine "understanding" but it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. |
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Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar, Kevin Yang, Arsha Nagrani, Cordelia Schmid, Andy Zeng, Trevor Darrell, Dan Klein
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Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)
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| Challenge: | Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges . |
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