vqa (visual question answering) v2

**VQA v2** (Visual Question Answering Version 2.0) is the **standard benchmark dataset for evaluating a model's ability to answer natural language questions about images** — specifically designed to reduce dataset biases found in the original VQA v1 by ensuring every question has complementary images with different answers. **What Is VQA v2?** - **Definition**: A large-scale dataset (~1.1M questions on COCO images). - **Core Feature**: Balanced pairs. For every question (e.g., "Is the man wearing a hat?"), there are images where the answer is "Yes" and others where it is "No". - **Goal**: Force the model to look at the image rather than guessing the most common answer from text statistics. **Why It Matters** - **Bias Correction**: In VQA v1, models could just answer "Yes" to "Do you see a..." and be right 80% of the time. VQA v2 fixes this. - **Gold Standard**: Has been the primary metric for multimodel progress from 2017 to 2023. - **Diversity**: Covers object counting, color identification, activity recognition, and reading. **VQA v2** is **the "ImageNet" of multimodal AI** — the historic measuring stick that tracked the rise of Transformers and the eventual solving of basic visual Q&A.