PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
November 29, 2022 Β· Declared Dead Β· π Web Search and Data Mining
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Authors
Zhihao Zhang, Siwen Luo, Junyi Chen, Sijia Lai, Siqu Long, Hyunsuk Chung, Soyeon Caren Han
arXiv ID
2211.15940
Category
cs.CV: Computer Vision
Cross-listed
cs.AI
Citations
2
Venue
Web Search and Data Mining
Last Checked
3 months ago
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. 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.
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