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Test-Time Training with Masked Autoencoders

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arxiv 2209.07522 v1 pith:YDFTMDEH submitted 2022-09-15 cs.CV cs.LG

Yossi Gandelsman , Yu Sun , Xinlei Chen , Alexei A. Efros This is my paper · ORCID
classification cs.CVcs.LG
keywords autoencodersdistributionmaskedtesttest-timetrainingadaptsbenchmarks
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Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision. In this paper, we use masked autoencoders for this one-sample learning problem. Empirically, our simple method improves generalization on many visual benchmarks for distribution shifts. Theoretically, we characterize this improvement in terms of the bias-variance trade-off.

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Cited by 1 Pith paper

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  1. Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Point-prompt-based test-time training adapts MedSAM to medical video segmentation, achieving 0.868 average Dice on VFSS anatomy segmentation.

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