Current instance segmentation methods can be categorized into segmentation-based methods that segment first then do clustering, and proposal-based methods that detect first then predict masks for each instance proposal using repooling. In this work, we propose a one-stage method, named EmbedMask, that unifies both methods by taking advantages of them. Like proposal-based methods, EmbedMask builds on top of detection models making it strong in detection capability. Meanwhile, EmbedMask applies extra embedding modules to generate embeddings for pixels and proposals, where pixel embeddings are guided by proposal embeddings if they belong to the same instance. Through this embedding coupling process, pixels are assigned to the mask of the proposal if their embeddings are similar. The pixel-level clustering enables EmbedMask to generate high-resolution masks without missing details from repooling, and the existence of proposal embedding simplifies and strengthens the clustering procedure to achieve high speed with higher performance than segmentation-based methods. Without any bells and whistles, EmbedMask achieves comparable performance as Mask R-CNN, which is the representative two-stage method, and can produce more detailed masks at a higher speed.

提出 EmbedMask 一种新的单阶段实例分割方法，结合了分割和提案两种方法的优点，通过像素嵌入和提案嵌入的耦合过程，实现像素级聚类，从而生成比基于分割和提案的方法更为细节丰富的高分辨率掩码，并在性能和速度上优于基于分割的方法和与 Mask R-CNN 相媲美的性能。

EmbedMask: 一阶段实例分割的嵌入耦合方法