Web: http://arxiv.org/abs/2206.08427

June 20, 2022, 1:10 a.m. | Ajay Subramanian, Sara Price, Omkar Kumbhar, Elena Sizikova, Najib J. Majaj, Denis G. Pelli

cs.LG updates on arXiv.org arxiv.org

The core of everyday tasks like reading and driving is active object
recognition. Attempts to model such tasks are currently stymied by the
inability to incorporate time. People show a flexible tradeoff between speed
and accuracy and this tradeoff is a crucial human skill. Deep neural networks
have emerged as promising candidates for predicting peak human object
recognition performance and neural activity. However, modeling the temporal
dimension i.e., the speed-accuracy tradeoff (SAT), is essential for them to
serve as useful …

accuracy arxiv benchmarking cv humans networks neural neural networks

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