@inproceedings{fcb205a714d149ab9d92461714797b78,
title = "PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals",
abstract = "We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. 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: Portability due to web-based and thus runs on almost any platform, A comprehensive data creation and processing technique, and ease of use on visual language pretrained models. The demo video can be found at https://youtu.be/iz44RZ1lF4s.",
keywords = "graphic user interface, visual linguistic pretrained model, visual question answering",
author = "Zhihao Zhang and Siwen Luo and Junyi Chen and Sijia Lai and Siqu Long and Hyunsuk Chung and Han, {Soyeon Caren}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 16th ACM International Conference on Web Search and Data Mining, WSDM 2023 ; Conference date: 27-02-2023 Through 03-03-2023",
year = "2023",
month = feb,
day = "27",
doi = "10.1145/3539597.3573039",
language = "English",
series = "WSDM - Proceedings of the ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery (ACM)",
booktitle = "WSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining",
address = "United States",
}