Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
This addresses robustness in object detection for real-world applications, offering incremental improvements to existing methods.
The paper tackles the problem of object detectors overfitting to in-distribution data and losing robustness to out-of-distribution shifts by proposing Relative Gradient Norm (RGN) to measure backbone vulnerability, and shows that high RGN correlates with lower OOD performance, with recipes for regularization and architectural changes leading to substantial improvements over baselines.
Building object detectors that are robust to domain shifts is critical for real-world applications. Prior approaches fine-tune a pre-trained backbone and risk overfitting it to in-distribution (ID) data and distorting features useful for out-of-distribution (OOD) generalization. We propose to use Relative Gradient Norm (RGN) as a way to measure the vulnerability of a backbone to feature distortion, and show that high RGN is indeed correlated with lower OOD performance. Our analysis of RGN yields interesting findings: some backbones lose OOD robustness during fine-tuning, but others gain robustness because their architecture prevents the parameters from changing too much from the initial model. Given these findings, we present recipes to boost OOD robustness for both types of backbones. Specifically, we investigate regularization and architectural choices for minimizing gradient updates so as to prevent the tuned backbone from losing generalizable features. Our proposed techniques complement each other and show substantial improvements over baselines on diverse architectures and datasets. Code is available at https://github.com/VisionLearningGroup/mind_back.