Jaeger：一个基于拼接的多Transformer VQA模型
Jaeger: A Concatenation-Based Multi-Transformer VQA Model
AI总结：
提出基于拼接的多Transformer VQA模型Jaegar，利用RoBERTa large和GPT2-xl提取特征并拼接降维，在PDF-VQA数据集Task C上取得有竞争力的性能。
摘要：
基于文档的视觉问答在语言意义消歧和细粒度多模态检索之间提出了一个具有挑战性的任务。尽管由于大型语言和开放世界先验模型的利用，基于文档的问答取得了令人鼓舞的进展，但仍然存在几个挑战，包括响应时间延长、推理时间延长以及匹配不精确。为了克服这些挑战，我们提出了Jaegar，一个基于拼接的多Transformer VQA模型。为了提取问题特征，我们利用RoBERTa large和GPT2-xl作为特征提取器的卓越能力。随后，我们将两个模型的输出进行拼接处理。此操作允许模型同时考虑来自不同来源的信息，增强其表示能力。通过利用预训练模型进行特征提取，我们的方法有潜力通过拼接放大这些模型的性能。拼接后，我们对输出特征进行降维，减少模型的计算效率和推理时间。实证结果表明，我们提出的模型在PDF-VQA数据集的Task C上取得了有竞争力的性能。如果用户添加任何新数据，他们应确保按照之前部分中提供的说明进行格式化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
