This repository implements multiple state-of-the-art Visual Question Answering (VQA) models from 2018, including bottom-up top-down attention, Bilinear Attention Networks (BAN), inter-intra modality attention, learning to count, and conditioned graph structures. It is built on Cyanogenoid's vqa-counting framework and provides a clean, modular codebase for training and evaluating VQA models on the VQA v2 dataset. The code includes data preprocessing scripts (feature extraction, vocabulary building), a flexible training pipeline with various loss functions and learning rate schedules, and evaluation scripts that compute per-category accuracy. While the code is well-structured for research purposes and contains reusable components like the counting module and fusion layers, it relies on deprecated PyTorch APIs (e.g., Variable), hardcoded paths, and lacks unit tests or environment definitions, making reproducibility challenging. The repository is primarily a research artifact for comparing attention mechanisms and counting strategies in VQA.