Convolutional Visual Prompt for Robust Visual Perception
This addresses robustness issues in vision models for applications requiring reliable performance on diverse data, but it is incremental as it builds on existing visual prompt methods.
The paper tackled the problem of vision models being vulnerable to out-of-distribution samples by introducing convolutional visual prompts for label-free test-time adaptation, resulting in improved robustness by up to 5.87% over several large-scale models.
Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale vision models, they rely on a high-dimensional additive vector and labeled data. This leads to overfitting when adapting models in a self-supervised test-time setting without labels. We introduce convolutional visual prompts (CVP) for label-free test-time adaptation for robust visual perception. The structured nature of CVP demands fewer trainable parameters, less than 1\% compared to standard visual prompts, combating overfitting. Extensive experiments and analysis on a wide variety of OOD visual perception tasks show that our approach is effective, improving robustness by up to 5.87% over several large-scale models.