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cs.MM2025
QuMAB: Query-based Multi-Annotator Behavior Modeling with Reliability under Sparse Labels
Liyun Zhang, Zheng Lian, Hong Liu +2
Multi-annotator learning traditionally aggregates diverse annotations to approximate a single ground truth, treating disagreements as noise. However, this paradigm faces fundamenta…
cs.MM2025
SimLabel: Similarity-Weighted Iterative Framework for Multi-annotator Learning with Missing Annotations
Liyun Zhang, Zheng Lian, Hong Liu +2
Multi-annotator learning (MAL) aims to model annotator-specific labeling patterns. However, existing methods face a critical challenge: they simply skip updating annotator-specific…
cs.MM2025
QuMATL: Query-based Multi-annotator Tendency Learning
Liyun Zhang, Zheng Lian, Hong Liu +2
Different annotators often assign different labels to the same sample due to backgrounds or preferences, and such labeling patterns are referred to as tendency. In multi-annotator…