论文标题
语义分割，具有主动半监督的表示学习
Semantic Segmentation with Active Semi-Supervised Representation Learning
论文作者
论文摘要
获得人的人均标签进行语义细分非常费力，通常使标记的数据集构造非常昂贵。在这里，我们努力通过一种结合了半监督和积极学习的新算法来克服这个问题，从而可以训练有效的语义分割算法，并具有明显较小的标记数据。为此，我们通过以自训练的方法来替换其平均教师方法，以一种自我训练的方法来替换其平均教师方法，从而扩展了先前的最新S4AL算法，从而可以通过嘈杂的标签来改善学习。我们通过添加对比度学习的头脑来进一步提高神经网络查询有用数据的能力，从而更好地理解场景中的对象，从而更好地了解积极学习的更好查询。我们评估了有关Camvid和CityScapes数据集的方法，这是用于语义细分的积极学习的事实上的标准。我们在CAMVID和CityScapes数据集上实现了95％以上的性能，分别仅利用标记数据的12.1％和15.1％。我们还在城市景观数据集上的现有独立半监督的学习方法中基准了我们的方法，并在没有任何铃铛或哨子的情况下实现了出色的性能。
Obtaining human per-pixel labels for semantic segmentation is incredibly laborious, often making labeled dataset construction prohibitively expensive. Here, we endeavor to overcome this problem with a novel algorithm that combines semi-supervised and active learning, resulting in the ability to train an effective semantic segmentation algorithm with significantly lesser labeled data. To do this, we extend the prior state-of-the-art S4AL algorithm by replacing its mean teacher approach for semi-supervised learning with a self-training approach that improves learning with noisy labels. We further boost the neural network's ability to query useful data by adding a contrastive learning head, which leads to better understanding of the objects in the scene, and hence, better queries for active learning. We evaluate our method on CamVid and CityScapes datasets, the de-facto standards for active learning for semantic segmentation. We achieve more than 95% of the network's performance on CamVid and CityScapes datasets, utilizing only 12.1% and 15.1% of the labeled data, respectively. We also benchmark our method across existing stand-alone semi-supervised learning methods on the CityScapes dataset and achieve superior performance without any bells or whistles.
