Vision And Text Transformer For Predicting Answerability On Visual Question Answering

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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2609.16565 [cs.CV]

(or arXiv:2609.16565v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.16565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/ICIP42928.2021.9506796 Focus to learn more DOI(s) linking to related resources

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