Inferring and Executing Programs for Visual Reasoning

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning... Inspired by module networks, this paper proposes a model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer. Both the program generator and the execution engine are implemented by neural networks, and are trained using a combination of backpropagation and REINFORCE. Using the CLEVR benchmark for visual reasoning, we show that our model significantly outperforms strong baselines and generalizes better in a variety of settings. read more

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Datasets


Introduced in the Paper:

CLEVR-Humans

Used in the Paper:

Visual Question Answering CLEVR SHAPES
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Visual Question Answering CLEVR IEP-700K Accuracy 96.9 # 12
Visual Question Answering CLEVR-Humans IEP-18K Accuracy 66.6 # 5

Methods