mlsanityData to 7 Oct 2026

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

arXiv 2211.15940 ↗First posted 29 Nov 2022cs.CV cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

12 of 18 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015VQA: Visual Question Answeringcited by 2,489
  3. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  4. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  5. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,291
  6. 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
  7. 2021SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answeringcited by 444
  8. 2022Vision-and-Language Pretrained Models: A Surveycited by 50
  9. 2020A Question-Centric Model for Visual Question Answering in Medical Imagingcited by 23
  10. 2022V-Doc : Visual questions answers with Documentscited by 19
  11. 2020Recommending Themes for Ad Creative Design via Visual-Linguistic Representationscited by 15
  12. 2020VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Taskscited by 12

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Citing papers per year: 2023: 1; 2024: 1.

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  1. 2025VQ-VA World: Towards High-Quality Visual Question-Visual Answering
  2. 2024Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
  3. 2025The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering
  4. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
  5. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities
  6. 2023Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering
  7. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
  8. 2024Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering