False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image. (arXiv:2211.07928v1 [cs.CV])

The existing SSCL of RSI is built based on constructing positive and negative
sample pairs. However, due to the richness of RSI ground objects and the
complexity of the RSI contextual semantics, the same RSI patches have the
coexistence and imbalance of positive and negative samples, which causing the
SSCL pushing negative samples far away while pushing positive samples far away,
and vice versa. We call this the sample confounding issue (SCI). To solve this
problem, we propose a False …

arxiv false image negative remote segmentation semantic sensing