论文标题
piggy背：审计的视觉问题回答了备份非深度学习专业人员的环境
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
论文作者
论文摘要
我们提出了一个视觉响应平台Piggyback，该平台使用户可以轻松地应用最先进的视觉语言预期模型。 Piggyback支持全面的视觉问题回答任务，特别是数据处理，模型微调和结果可视化。我们集成了视觉语言模型，该模型由Huggingface介绍，后者是深度学习技术的开源API平台；但是，如果没有编程技能或深入的学习理解，就无法运行。因此，我们的Piggyback支持易于使用的基于浏览器的用户界面，并为通用用户和域专家提供了几种深度学习的视觉语言模型。 Piggyback包括以下好处：在MIT许可下的自由供应，由于基于Web而引起的可移植性，因此在几乎任何平台上运行，全面的数据创建和处理技术，以及易于对基于深度学习的视觉语言预测的模型的易用性。该演示视频可在YouTube上找到，可以在https://youtu.be/iz44rz1lf4s上找到。
We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. The PiggyBack supports the full stack of visual question answering tasks, specifically data processing, model fine-tuning, and result visualisation. We integrate visual-language models, pretrained by HuggingFace, an open-source API platform of deep learning technologies; however, it cannot be runnable without programming skills or deep learning understanding. Hence, our PiggyBack supports an easy-to-use browser-based user interface with several deep learning visual language pretrained models for general users and domain experts. The PiggyBack includes the following benefits: Free availability under the MIT License, Portability due to web-based and thus runs on almost any platform, A comprehensive data creation and processing technique, and ease of use on deep learning-based visual language pretrained models. The demo video is available on YouTube and can be found at https://youtu.be/iz44RZ1lF4s.
