  Open-world long-tailed semi-supervised learning (OLSSL) has increasingly
attracted attention. However, existing OLSSL algorithms generally assume that
the distributions between known and novel categories are nearly identical.
Against this backdrop, we construct a more \emph{Realistic Open-world
Long-tailed Semi-supervised Learning} (\textbf{ROLSSL}) setting where there is
no premise on the distribution relationships between known and novel
categories. Furthermore, even within the known categories, the number of
labeled samples is significantly smaller than that of the unlabeled samples, as
acquiring valid annotations is often prohibitively costly in the real world.
Under the proposed ROLSSL setting, we propose a simple yet potentially
effective solution called dual-stage post-hoc logit adjustments. The proposed
approach revisits the logit adjustment strategy by considering the
relationships among the frequency of samples, the total number of categories,
and the overall size of data. Then, it estimates the distribution of unlabeled
data for both known and novel categories to dynamically readjust the
corresponding predictive probabilities, effectively mitigating category bias
during the learning of known and novel classes with more selective utilization
of imbalanced unlabeled data. Extensive experiments on datasets such as
CIFAR100 and ImageNet100 have demonstrated performance improvements of up to
50.1\%, validating the superiority of our proposed method and establishing a
strong baseline for this task. For further researches, the anonymous link to
the experimental code is at
\href{https://github.com/heyuanpengpku/ROLSSL}{\textcolor{brightpink}{https://github.com/heyuanpengpku/ROLSSL}}
