论文标题
一个积极且对比度的学习框架，用于细粒度的越野语义细分
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
论文作者
论文摘要
具有精细颗粒标签的越野语义细分对于自动驾驶汽车了解驾驶场景是必要的，因为粗粒的道路检测无法满足具有各种机械性能的越野车辆。在越野场景中，细粒度的语义细分通常由于模棱两可的性质环境而没有统一的类别定义，并且像素标签的成本非常高。此外，由于各种降水，温度，脱叶等，越野场景的语义性能可能会非常多，以应对这些挑战，这项研究提出了一种不依赖于像素的标签，而仅对基于斑块的基于贴片的模型学习的弱注释。不需要预定义的语义类别，基于对比的学习特征表示和自适应聚类将从场景数据中发现类别模型。为了积极适应新场景，提出了一种风险评估方法，以发现并选择具有高风险预测的硬帧以进行补充标签，以便有效地更新模型。在我们自行开发的越野数据集和DeepScene数据集上进行的实验表明，只有数十个弱标记的框架只能学习细粒度的语义细分，并且该模型可以通过薄弱的监督在场景中有效地适应几乎相同的表现水平，而与典型的完全监督的基准相同。
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.
