Vision And Text Transformer For Predicting Answerability On Visual Question Answering

Tung Le; Huy Tien Nguyen; Le Minh Nguyen

doi:10.1109/icip42928.2021.9506796

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arXiv · 2609.16565

Vision And Text Transformer For Predicting Answerability On Visual Question Answering

Tung Le·Huy Tien Nguyen·Le Minh Nguyen

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Abstract

Answerability on Visual Question Answering is a novel and attractive task to predict answerable scores between images and questions in multi-modal data. Existing works often utilize a binary mapping from visual question answering systems into Answerability. It does not reflect the essence of this problem. Together with our consideration of Answerability in a regression task, we propose VT-Transformer, which exploits visual and textual features through Transformer architecture. Experimental results on VizWiz 2020 dataset show the effectiveness and robustness of VT-Transformer for Answerability on Visual Question Answering when comparing with competitive baselines.

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BibTeX RIS

Tung Le, Huy Tien Nguyen, Le Minh Nguyen. 2026-09-15. Vision And Text Transformer For Predicting Answerability On Visual Question Answering. https://doi.org/10.1109/icip42928.2021.9506796

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