Curiosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning

The per-label distribution is typically long-tailed (Tarekegn et al., 2021; De Alvis and Seneviratne, 2024): head labels dominate while tail labels appear sporadically. This imbalance is exacerbated in MLC because (i) co-occurring labels make resampling risky, and (ii) metrics like mAP favor head labels. As a result, standard optimizers (Ridnik et al., 2021) often learn head-biased boundaries, achieving high scores while failing on tail labels-problematic for safety-critical applications. In practice the per-label sample counts follow a heavy-tailed distribution: a handful of head labels dominate the data, whereas the vast majority of tail labels appear only sporadically, as shown in Figure 1. This long-tail imbalance (Tarekegn et al., 2021; De Alvis and Seneviratne, 2024) is particularly severe in the multi-label regime because (i) multiple labels co-occur within a single instance, so naïve resampling can destroy cross-label correlations, and (ii) evaluation metrics such as mAP or micro-F1 are disproportionately influenced by head labels, starving tail classes of gradient signal. Consequently, conventional optimizers (Ridnik et al., 2021) that target average loss or accuracy often learn a head-biased decision boundary, yielding high headline scores while silently failing on the tail-an outcome that is unacceptable in safety-critical or comprehensive retrieval scenarios(Barandas et al., 2024).