PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals

Autor:	Zhang, Zhihao, Luo, Siwen, Chen, Junyi, Lai, Sijia, Long, Siqu, Chung, Hyunsuk, Han, Soyeon Caren
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by WSDM 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.15940 Zobrazit plný text záznamu View this record from Arxiv