Visual Question Answering (VQA) is a task that requires computers to give
correct answers for the input questions based on the images. This task can be
solved by humans with ease but is a challenge for computers. The
VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the
multilingual domain on a newly released dataset: UIT-EVJVQA, in which the
questions and answers are written in three different languages: English,
Vietnamese and Japanese. We approached the challenge as a sequence-to-sequence
learning task, in which we integrated hints from pre-trained state-of-the-art
VQA models and image features with Convolutional Sequence-to-Sequence network
to generate the desired answers. Our results obtained up to 0.3442 by F1 score
on the public test set, 0.4210 on the private test set, and placed 3rd in the
competition.

本研究利用已预训练的最新 VQA 模型和卷积序列到序列网络整合图像特征，作为 VLSP2022-EVJVQA 多语言场景下，视觉问答任务的解决方案，在公共测试集上达到了 F1 分数 0.3442，在私人测试集上达到了 0.4210，排名第三。