Looking Beyond Single Images for Contrastive Semantic Segmentation Learning - Supplementary Material - 1 Additional results 1.1 Controlled experiment on auxiliary label generation

Table 1 reports the results of a controlled experiment evaluating different components in our framework for auxiliary label generation. Positive correspondences are generated by matching pixels across different augmentations of the same image. With respect to the clustering algorithm, K-means performs better than DBSCAN (#4 vs. #5), which is We show qualitative results, comparing different feature extractors in Figure 1. DBSCAN is limited by the memory and computational complexity. Corresponding qualitative results are shown in Figure 3. Tables 3-5 show We observe the best performance when 5% outliers are removed.