Visual Turing test for computer vision systems

Proceedings of the National Academy of Sciences of the United States of America
Donald GemanLaurent Younes

Abstract

Today, computer vision systems are tested by their accuracy in detecting and localizing instances of objects. As an alternative, and motivated by the ability of humans to provide far richer descriptions and even tell a story about an image, we construct a "visual Turing test": an operator-assisted device that produces a stochastic sequence of binary questions from a given test image. The query engine proposes a question; the operator either provides the correct answer or rejects the question as ambiguous; the engine proposes the next question ("just-in-time truthing"). The test is then administered to the computer-vision system, one question at a time. After the system's answer is recorded, the system is provided the correct answer and the next question. Parsing is trivial and deterministic; the system being tested requires no natural language processing. The query engine employs statistical constraints, learned from a training set, to produce questions with essentially unpredictable answers-the answer to a question, given the history of questions and their correct answers, is nearly equally likely to be positive or negative. In this sense, the test is only about vision. The system is designed to produce streams of questions th...Continue Reading

References

Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Jul 17, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pedro F FelzenszwalbDeva Ramanan

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Citations

Oct 10, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Ali BorjiJia Li
Jun 3, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Qi WuAnton van den Hengel
Jul 11, 2018·IEEE Transactions on Neural Networks and Learning Systems· Fuyong Xing Lin Yang
Aug 21, 2019·Sensors·Darius DirvanauskasRafal Scherer
Mar 1, 2020·Annals of the New York Academy of Sciences·Gabriel Kreiman, Thomas Serre
Mar 21, 2018·Proceedings of the National Academy of Sciences of the United States of America·Carlos F Benitez-QuirozAleix M Martinez
Dec 12, 2018·PLoS Computational Biology·Nicholas BakerPhilip J Kellman
Dec 19, 2018·Proceedings of the National Academy of Sciences of the United States of America·Lichao ChenVwani Roychowdhury
Dec 13, 2019·Frontiers in Artificial Intelligence·Kushal KafleChristopher Kanan
Oct 15, 2020·Proceedings of the National Academy of Sciences of the United States of America·Chaz Firestone
Jul 6, 2021·Language and Linguistics Compass·Raffaella Bernardi, Sandro Pezzelle

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