When Complete Becomes Partial: Multi-Label Active Learning under Label Expansion

Multi-label active learning aims to train accurate classifiers with less labeling effort by selectively querying instances that might be associated with multiple labels. However, previous studies often assume a fixed set of target categories, without considering the introduction of new categories during annotation. When new categories are introduced, annotations that were complete under the previous label set become partial, leaving unverified labels on historical instances. Therefore, learning under this expansion requires deciding both which unqueried instances to annotate and which historical instances to revisit within a limited query budget. To this end, we introduce Retrospective Multi-Label Active Learning (ReMuAL), a framework that combines first-time queries with selective revisits in a collaborative fashion. Specifically, first-time query feedback updates the model and label relationships, which are then used with verified historical labels to select revisits. Empirically, ReMuAL achieves gains up to 4.67 and 6.45 percentage points in Checklist mAP and full-trajectory AULC respectively over multiple benchmarks.