Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects. Motivated by safety reasons, we address the video class agnostic segmentation task, which considers unknown objects outside the closed set of known classes in our training data. We propose a novel auxiliary contrastive loss to learn the segmentation of known classes and unknown objects. Unlike previous work in contrastive learning that samples the anchor, positive and negative examples on an image level, our contrastive learning method leverages pixel-wise semantic and temporal guidance. We conduct experiments on Cityscapes-VPS by withholding four classes from training and show an improvement gain for both known and unknown objects segmentation with the auxiliary contrastive loss. We further release a large-scale synthetic dataset for different autonomous driving scenarios that includes distinct and rare unknown objects. We conduct experiments on the full synthetic dataset and a reduced small-scale version, and show how contrastive learning is more effective in small scale datasets. Our proposed models, dataset, and code will be released at https://github.com/MSiam/video_class_agnostic_segmentation.
翻译:自主驾驶中的语义分解主要侧重于从大型数据中学习,使用一组封闭的已知类别,而不考虑未知物体。出于安全考虑,我们处理视频类不可知分解任务,在培训数据中考虑到一组封闭已知类别之外的未知物体;我们提出一个新的辅助性对比性损失,以学习已知类别和未知对象的分解。与以往的对比性学习工作不同,我们通过对图像层的锚、正和负示例进行抽样抽样,我们对比性学习方法的杠杆像素、灵巧语义和时间指导。我们在城市景象-VPS上进行实验,从培训中扣下四个类,显示已知和未知对象分解的已知和未知的辅助对比性损失的改善收益。我们进一步为不同自主驱动情景发布一个大型合成数据集,其中包括独特和罕见的未知物体。我们在全合成数据集和减少的小型版本上进行实验,并显示在小型数据集中对比性学习的效果如何。我们提议的模型、数据集和代码将在https://github.com/MSiaam_sion_cal_cionalment_nomentmentmentation.