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Transparency by Design: Closing the Gap Between Performance andInterpretability in Visual ReasoningDavid Mascharka 1 Philip Tran2 Ryan Soklaski1 Arjun Majumdar 11 MIT Lincoln Laboratory 2 Planck Aerosystems question answering requires high-order reason-ing about an image, which is a fundamental capabilityneeded by machine systems to follow complex , modular networks have been shown to be an ef-fective framework for performing visual reasoning modular networks were initially designed with a de-gree of model Transparency , their performance on complexvisual reasoning benchmarks was lacking. Current state-of-the-art approaches do not provide an effective mecha-nism for understanding the reasoning process. In this paper,we close the performance gap Between interpretable modelsand state-of-the-art visual reasoning methods. We proposea set of visual-reasoning primitives which, when composed,manifest as a model capable of performing complex reason-ing tasks in an explicitly-interpretable manner.
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning David Mascharka 1Philip Tran2 Ryan Soklaski Arjun Majumdar 1MIT Lincoln Laboratoryy 2Planck Aerosystemsz ffirst.lastg@ll.mit.edu, phil@planckaero.com
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