Abstract: Highlights•Proposes a method to address background shift problems in all training samples.•Utilizes annotators to complete missing annotations for past and new classes.•Implements a class-equilibrium sampling strategy to long-tail challenges.•Demonstrates superior performance of TAGA over the state-of-the-art CSS methods.
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