Document-based Visual Question Answering poses a challenging task between
linguistic sense disambiguation and fine-grained multimodal retrieval. Although
there has been encouraging progress in document-based question answering due to
the utilization of large language and open-world prior models\cite{1}, several
challenges persist, including prolonged response times, extended inference
durations, and imprecision in matching. In order to overcome these challenges,
we propose Jaegar, a concatenation-based multi-transformer VQA model. To derive
question features, we leverage the exceptional capabilities of RoBERTa
large\cite{2} and GPT2-xl\cite{3} as feature extractors. Subsequently, we
subject the outputs from both models to a concatenation process. This operation
allows the model to consider information from diverse sources concurrently,
strengthening its representational capability. By leveraging pre-trained models
for feature extraction, our approach has the potential to amplify the
performance of these models through concatenation. After concatenation, we
apply dimensionality reduction to the output features, reducing the model's
computational effectiveness and inference time. Empirical results demonstrate
that our proposed model achieves competitive performance on Task C of the
PDF-VQA Dataset. If the user adds any new data, they should make sure to style
it as per the instructions provided in previous sections.

本文介绍了 Jaegar，一种基于连接的多转换器 VQA 模型，用于解决基于文档的视觉问答中的挑战。该模型利用 RoBERTa large 和 GPT2-xl 作为特征提取器，并通过将两个模型的输出进行连接来加强其表示能力，以减少计算复杂性和推理时间。实证结果表明，该模型在 PDF-VQA 数据集的 C 任务上具有竞争力的性能。