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The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged.
Individual comparisons by ranking methods
Frank Wilcoxon · 1992
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Kimmo Kärkkäinen and Jungseock Joo · 2019
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Inioluwa Deborah Raji and Joy Buolamwini · 2019
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Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
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Masked autoencoders are scalable vision learners
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Partial success in closing the gap between human and machine vision
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Natural adversarial examples
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Scaling vision transformers
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Contrastive learning of medical visual representations from paired images and text
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Quantifying and mitigating the impact of label errors on model disparity metrics
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Scaling vision transformers to 22 billion parameters
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Scaling language-image pre-training via masking, 2023
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On efficient losses for distributed contrastive learning
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