Adapt, But Don't Forget: Fine-Tuning and Contrastive Routing for Lane Detection under Distribution Shift
This addresses a domain-specific problem for autonomous driving systems by enabling parameter-efficient adaptation to new datasets, though it is incremental as it builds on existing fine-tuning and routing techniques.
The paper tackles the problem of catastrophic forgetting in lane detection models during fine-tuning across datasets with distribution shifts, achieving near-optimal F1-scores with significantly fewer parameters than training separate models.
Lane detection models are often evaluated in a closed-world setting, where training and testing occur on the same dataset. We observe that, even within the same domain, cross-dataset distribution shifts can cause severe catastrophic forgetting during fine-tuning. To address this, we first train a base model on a source distribution and then adapt it to each new target distribution by creating separate branches, fine-tuning only selected components while keeping the original source branch fixed. Based on a component-wise analysis, we identify effective fine-tuning strategies for target distributions that enable parameter-efficient adaptation. At inference time, we propose using a supervised contrastive learning model to identify the input distribution and dynamically route it to the corresponding branch. Our framework achieves near-optimal F1-scores while using significantly fewer parameters than training separate models for each distribution.