Developed a Visual Question Answering (VQA) demonstration notebook for the beyond-the-pixels-emerging-computer-vision-research-topics-fa24 repository, focusing on reproducibility and onboarding for vision-and-language research. Leveraged Python and Jupyter Notebook to implement a BLIP-2-based VQA pipeline on the VQA v2.0 dataset, including environment setup, pre-trained model loading, and step-by-step instructions for running and visualizing results. Enhanced project maintainability by cleaning up obsolete assets and standardizing documentation, such as renaming and aligning README files. This work streamlined experimentation for researchers, reduced confusion during onboarding, and established a clear baseline for future VQA enhancements using deep learning and computer vision techniques.
November 2024: Delivered a focused VQA demonstration path for the beyond-the-pixels project and cleaned repository assets to improve onboarding and maintainability. Implemented a VQA Demo Notebook using BLIP-2 on the VQA v2.0 dataset, including environment setup, loading a pre-trained model, and clear steps to run and visualize VQA results. Performed targeted documentation and asset cleanup to remove obsolete files and standardize readmes, reducing confusion and future maintenance effort. This work enhances reproducibility for researchers, accelerates experimentation with vision-and-language tasks, and establishes a solid baseline for future VQA enhancements.
November 2024: Delivered a focused VQA demonstration path for the beyond-the-pixels project and cleaned repository assets to improve onboarding and maintainability. Implemented a VQA Demo Notebook using BLIP-2 on the VQA v2.0 dataset, including environment setup, loading a pre-trained model, and clear steps to run and visualize VQA results. Performed targeted documentation and asset cleanup to remove obsolete files and standardize readmes, reducing confusion and future maintenance effort. This work enhances reproducibility for researchers, accelerates experimentation with vision-and-language tasks, and establishes a solid baseline for future VQA enhancements.
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