As a proposal-free approach, instance segmentation through pixel embedding
learning and clustering is gaining more emphasis. Compared with bounding box
refinement approaches, such as Mask R-CNN, it has potential advantages in
handling complex shapes and dense objects. In this work, we propose a simple,
yet highly effective, architecture for object-aware embedding learning. A
distance regression module is incorporated into our architecture to generate
seeds for fast clustering. At the same time, we show that the features learned
by the distance regression module are able to promote the accuracy of learned
object-aware embeddings significantly. By simply concatenating features of the
distance regression module to the images as inputs of the embedding module, the
mSBD scores on the CVPPP Leaf Segmentation Challenge can be further improved by
more than 8% compared to the identical set-up without concatenation, yielding
the best overall result amongst the leaderboard at CodaLab.

本文主要研究一种无需提议的实例分割方法，通过像素嵌入学习和聚类来识别图像中的物体，并提出一种包含距离回归模块的简单有效的体系结构，用于生成快速聚类的种子，并且通过将距离回归模块的特征与图像进行拼接，可以显著提高学习到的嵌入特征的准确性。该方法在 CVPPP Leaf Segmentation Challenge 上的结果是排行榜中最好的。