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Found 3 papers, 1 papers with code
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks
Using FLOPs as an analog for reaction time, we compare networks with humans on curve-fit error, category-wise correlation, and curve steepness, and conclude that cascaded dynamic neural networks are a promising model of human reaction time in object recognition tasks.
Anytime Prediction as a Model of Human Reaction Time
We conclude that Anytime classification (i. e. early exits) is a promising model for human reaction time in recognition tasks.
Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models
Relative to the attention-based (Attn) model, we discover that the connectionist temporal classification (CTC) model is more robust to noise and occlusion, and better at generalizing to different word lengths.
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