  The fine-tuning paradigm in addressing long-tail learning tasks has sparked
significant interest since the emergence of foundation models. Nonetheless, how
fine-tuning impacts performance in long-tail learning was not explicitly
quantified. In this paper, we disclose that heavy fine-tuning may even lead to
non-negligible performance deterioration on tail classes, and lightweight
fine-tuning is more effective. The reason is attributed to inconsistent class
conditions caused by heavy fine-tuning. With the observation above, we develop
a low-complexity and accurate long-tail learning algorithms LIFT with the goal
of facilitating fast prediction and compact models by adaptive lightweight
fine-tuning. Experiments clearly verify that both the training time and the
learned parameters are significantly reduced with more accurate predictive
performance compared with state-of-the-art approaches. The implementation code
is available at https://github.com/shijxcs/LIFT.
