PEEK: Partial Label Learning Empowered by External Knowledge

Partial Label Learning (PLL) is a challenging weakly supervised learning problem that aims to identify the ground-truth label from a candidate set containing exactly one true label and multiple false positives. Despite steady progress, existing PLL methods predominantly rely on closed-world supervision derived from the training data itself, leaving a clear gap in how to effectively incorporate external knowledge—especially the open-world semantic priors emerging from recent large-scale foundation models. In this paper, we first establish a theory which shows that external guidance should be assigned a higher weight in the early stage of training and down-weighted in the later, enabling a smooth transition from knowledge-assisted disambiguation to self-driven refinement. Building on this insight, we propose PEEK, a computationally efficient framework that leverages the open-world semantic knowledge of a frozen VLM (e.g., CLIP). PEEK dynamically fuses the model’s confidence with the VLM-derived confidence, either via simple global weighting or instance-adaptive weighting, thereby enabling an adaptive transition from external guidance to internal self-evolution. As a versatile plug-and-play module, PEEK significantly accelerates convergence and improves a wide range of state-of-the-art PLL baselines on standard benchmarks as well as challenging fine-grained datasets, without introducing additional training overhead.