We had human subjects perform a one-out-of-six class action recognition task from video stimuli while undergoing functional magnetic resonance imaging (fMRI). Support-vector machines (SVMs) were trained on the recovered brain scans to classify actions observed during imaging, yielding average classification accuracy of 69.73% when tested on scans from the same subject and of 34.80% when tested on scans from different subjects. An apples-to-apples comparison was performed with all publicly available software that implements state-of-the-art action recognition on the same video corpus with the same cross-validation regimen and same partitioning into training and test sets, yielding classification accuracies between 31.25% and 52.34%. This indicates that one can read people's minds better than state-of-the-art computer-vision methods can perform action recognition. © 2014 Springer International Publishing.
CITATION STYLE
Barbu, A., Barrett, D. P., Chen, W., Siddharth, N., Xiong, C., Corso, J. J., … Wilbur, R. B. (2014). Seeing is worse than believing: Reading people’s minds better than computer-vision methods recognize actions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8693 LNCS, pp. 612–627). Springer Verlag. https://doi.org/10.1007/978-3-319-10602-1_40
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