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.