Affinity, which represents whether two pixels belong to a same instance, is an equivalent representation to the instance segmentation labels. Conventional works do not make an explicit exploration on the affinity. In this article, we present two instance segmentation schemes based on pixel affinity information and show the effectiveness of affinity in both aspects. For proposal-free method, we predict pixel affinity for each image and then propose a simple yet effective graph merge algorithm to cluster pixels into instances. It shows that the affinity is powerful as an instance-relevant information to guide the clustering procedure in proposal-free instance segmentation. For proposal-based methods, we extend conventional framework with affinity head and introduce affinity as attached supervision in training phase. Without any additional inference cost, we can improve the performance of existing proposal-based instance segmentation methods, which shows that the affinity can also be applied as an auxiliary loss and training with such extra loss is beneficial to the training progress. Experimental results show that our schemes achieve comparable performance to other state-of-the-art instance segmentation methods. With Cityscapes training data, the proposed proposal-free method achieves 28.8 AP and the proposal-based method gets 27.2 AP both on test sets.

中文翻译：

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精确实例分割的亲和推导

亲和度，表示两个像素是否属于同一个实例，是实例分割标签的等价表示。常规作品没有对亲和性进行明确的探索。在本文中，我们提出了两种基于像素亲和力信息的实例分割方案，并展示了亲和力在两个方面的有效性。对于无提议方法，我们预测每个图像的像素亲和力，然后提出一种简单而有效的图合并算法来将像素聚类到实例中。它表明亲和性作为实例相关信息在指导无提议实例分割中的聚类过程方面是强大的。对于基于提案的方法，我们扩展了具有亲和力头的传统框架，并在训练阶段引入了亲和力作为附加监督。在没有任何额外推理成本的情况下，我们可以提高现有基于提议的实例分割方法的性能，这表明亲和力也可以用作辅助损失，使用这种额外损失进行训练有利于训练进度。实验结果表明，我们的方案实现了与其他最先进的实例分割方法相当的性能。使用 Cityscapes 训练数据，建议的无提案方法在测试集上都达到了 28.8 AP，而基于提案的方法在测试集上都获得了 27.2 AP。实验结果表明，我们的方案实现了与其他最先进的实例分割方法相当的性能。使用 Cityscapes 训练数据，建议的无提案方法在测试集上都达到了 28.8 AP，而基于提案的方法在测试集上都获得了 27.2 AP。实验结果表明，我们的方案实现了与其他最先进的实例分割方法相当的性能。使用 Cityscapes 训练数据，建议的无提案方法在测试集上都达到了 28.8 AP，而基于提案的方法在测试集上都获得了 27.2 AP。