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About this paper
Drew A. Hudson, Christopher D. Manning
GQA is a visual-question-answering benchmark that generates compositional questions and reasoning programs from scene graphs of real photographs. The authors balance answer distributions and use the programs to assess model accuracy and consistency. Its balanced dataset contains 1.7 million questions; on its test set, MAC reaches 54.06% accuracy versus 89.3% for humans, without establishing that balancing improves visual reasoning.
Key insights
- Addressing dataset biases and improving compositional reasoning are critical for advancing real-world visual understanding in AI.
- Structured scene graph representations can bridge the gap between synthetic control and real-world visual complexity.
- Detailed question annotations are essential for diagnostic analysis, allowing researchers to understand why models succeed or fail.
Summary by alphaXiv.
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