The visual world naturally exhibits a long-tailed distribution of open
classes, which poses great challenges to modern visual systems. Existing
approaches either perform class re-balancing strategies or directly improve
network modules to address the problem. However, they still train models with a
finite set of predefined labels, limiting their supervision