Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions
Author(s)Barbu, Andrei; Barrett, Daniel P.; Chen, Wei; Narayanaswamy, Siddharth; Xiong, Caiming; Corso, Jason J.; Fellbaum, Christiane D.; Hanson, Catherine; Hanson, Stephen Jose; Helie, Sebastien; Malaia, Evguenia; Pearlmutter, Barak A.; Siskind, Jeffrey Mark; Talavage, Thomas Michael; Wilbur, Ronnie B.; ... Show more Show less
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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.
CBMM Memo Series;012
Object Recognition, Vision, Support-Vector Machines (SVMs)
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