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Privacy Preserving Visual Question Answering

2022-02-15 20:22:50
Cristian-Paul Bara, Qing Ping, Abhinav Mathur, Govind Thattai, Rohith MV, Gaurav S. Sukhatme
arXiv_CV
       
Abstract

We introduce a novel privacy-preserving methodology for performing Visual Question Answering on the edge. Our method constructs a symbolic representation of the visual scene, using a low-complexity computer vision model that jointly predicts classes, attributes and predicates. This symbolic representation is non-differentiable, which means it cannot be used to recover the original image, thereby keeping the original image private. Our proposed hybrid solution uses a vision model which is more than 25 times smaller than the current state-of-the-art (SOTA) vision models, and 100 times smaller than end-to-end SOTA VQA models. We report detailed error analysis and discuss the trade-offs of using a distilled vision model and a symbolic representation of the visual scene.

Abstract (translated)

URL

https://arxiv.org/abs/2202.07712

PDF

https://arxiv.org/pdf/2202.07712.pdf

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