During November 2024, Moe 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. Using Python and Jupyter Notebook, Moe implemented a BLIP-2-based VQA pipeline on the VQA v2.0 dataset, detailing environment setup, model loading, and step-by-step usage for researchers. Moe also improved project maintainability by cleaning obsolete assets and standardizing documentation, including renaming and removing outdated README files. This work provided a clear, runnable baseline for vision-and-language tasks, streamlining experimentation and reducing confusion for future contributors, with depth in both technical implementation and project organization.
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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