Inferring and Executing Programs for Visual Reasoning (clevr-iep)

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, the paper, Inferring and Executing Programs for Visual Reasoning  Justin JohnsonBharath HariharanLaurens van der MaatenJudy HoffmanFei-Fei LiLarry ZitnickRoss Girshick  from ICCV 2017 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.

 

This code, including pretrained and running models, was used for the work presented in the paper Inferring and Executing Programs for Visual Reasoning  Justin JohnsonBharath HariharanLaurens van der MaatenJudy HoffmanFei-Fei LiLarry ZitnickRoss Girshick  from ICCV 2017.