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| tags: | |
| - visual-question-answering-for-fashion-context | |
| sdk: gradio | |
| license: apache-2.0 | |
| widget: | |
| - text: "VQA system" | |
| src: "testing.jpg" | |
| # This is a simple VQA system using Hugging Face, PyTorch and VQA models | |
| ------------- | |
| In this repository we created a simple VQA system capable of recognize spatial and context information of fashion images (e.g. clothes color and details). | |
| The project was based in this paper **FashionVQA: A Domain-Specific Visual Question Answering System** [[1]](#1). We also used the VQA pre-trained model from **BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation** [[]](#2) to make the model finetuning the two new models. | |
| We used the datasets **Deep Fashion with Masks** available in <https://huggingface.co/datasets/SaffalPoosh/deepFashion-with-masks> and the **Control Net Dataset** available in <https://huggingface.co/datasets/ldhnam/deepfashion_controlnet>. | |
| ## References | |
| <a id="1">[1]</a> | |
| Min Wang and Ata Mahjoubfar and Anupama Joshi, 2022 | |
| FashionVQA: A Domain-Specific Visual Question Answering System | |
| <a id="2">[2]</a> | |
| Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi, 2022 | |
| BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation | |