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Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases

NeurIPS 2021 · Vision-language and multimodal › Human and embodied modeling · approach: Human perception modeling · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use eccentricity-dependent pooling instead of VGG16 max-pooling (ablation)eccNET · Six visual-search asymmetry experimentsaverage Asymmetry IndexHelped
Combine top-down modulation across multiple layers instead of using only layer 18 to layer 17 (ablation)eccNET · Six visual-search asymmetry experimentsaverage Asymmetry Index: 0.084Helped
Use a ResNet152 backbone instead of VGG16 (ablation)eccNET · Six visual-search asymmetry experimentsexperiments with positive asymmetry index: four out of the sixHurt
Train from scratch on MNIST instead of ImageNeteccNET · Lighting Direction (Experiment 2)visual-search asymmetryHurt
Train from scratch on ImageNet rotated by 90 degreeseccNET · Lighting Direction (Experiment 2)polarity of asymmetryMixed
Train on ImageNet after a fisheye transform that increases curves and reduces straight lineseccNET · Curvature (Experiment 1)polarity of asymmetryMixed
Increase the proportion of straight lines in the training dataeccNET · Curvature (Experiment 1)polarity of asymmetryNo clear effect
Train on Places365 instead of ImageNeteccNET · Visual-search asymmetry experimentspolarity of asymmetryMixed
Train on rotated Places365 instead of ImageNeteccNET · Visual-search asymmetry experimentspolarity of asymmetryMixed
Use eccNET rather than the baseline modelsvisual-search models · Six visual-search asymmetry experimentsagreement with human asymmetry: 5 out of 6Helped
Use target-dependent top-down features rather than bottom-up saliency alonevisual-search models · Six visual-search asymmetry experimentsagreement with human asymmetryHelped
Use natural-image features from IVSN rather than chance, pixel matching, or GBVSvisual-search models · Six visual-search asymmetry experimentsAsymmetry IndexHelped

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