An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
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Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-road vehicles with various mechanical properties. Fine-grained semantic segmentation in off-road scenes usually has no unified category definition due to ambiguous nature environments, and the cost of pixel-wise labeling is extremely high. Furthermore, semantic properties of off-road scenes can be very changeable due to various precipitations, temperature, defoliation, etc. To address these challenges, this research proposes an active and contrastive learning-based method that does not rely on pixel-wise labels, but only on patch-based weak annotations for model learning. There is no need for predefined semantic categories, the contrastive learning-based feature representation and adaptive clustering will discover the category model from scene data. In order to actively adapt to new scenes, a risk evaluation method is proposed to discover and select hard frames with high-risk predictions for supplemental labeling, so as to update the model efficiently. Experiments conducted on our self-developed off-road dataset and DeepScene dataset demonstrate that fine-grained semantic segmentation can be learned with only dozens of weakly labeled frames, and the model can efficiently adapt across scenes by weak supervision, while achieving almost the same level of performance as typical fully supervised baselines.
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