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Do Explanations make VQA Models more Predictable to a Human?

2018-10-29 19:14:26
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, Devi Parikh
arXiv_AI
     
Abstract

A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model -- its responses as well as failures -- more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.

Abstract (translated)

URL

https://arxiv.org/abs/1810.12366

PDF

https://arxiv.org/pdf/1810.12366.pdf

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