  As one of the most challenging and practical segmentation tasks, open-world
semantic segmentation requires the model to segment the anomaly regions in the
images and incrementally learn to segment out-of-distribution (OOD) objects,
especially under a few-shot condition. The current state-of-the-art (SOTA)
method, Deep Metric Learning Network (DMLNet), relies on pixel-level metric
learning, with which the identification of similar regions having different
semantics is difficult. Therefore, we propose a method called region-aware
metric learning (RAML), which first separates the regions of the images and
generates region-aware features for further metric learning. RAML improves the
integrity of the segmented anomaly regions. Moreover, we propose a novel
meta-channel aggregation (MCA) module to further separate anomaly regions,
forming high-quality sub-region candidates and thereby improving the model
performance for OOD objects. To evaluate the proposed RAML, we have conducted
extensive experiments and ablation studies on Lost And Found and Road Anomaly
datasets for anomaly segmentation and the CityScapes dataset for incremental
few-shot learning. The results show that the proposed RAML achieves SOTA
performance in both stages of open world segmentation. Our code and appendix
are available at https://github.com/czifan/RAML.
