  As a promising solution of reducing annotation cost, training multi-label
models with partial positive labels (MLR-PPL), in which merely few positive
labels are known while other are missing, attracts increasing attention. Due to
the absence of any negative labels, previous works regard unknown labels as
negative and adopt traditional MLR algorithms. To reject noisy labels, recent
works regard large loss samples as noise but ignore the semantic correlation
different multi-label images. In this work, we propose to explore semantic
correlation among different images to facilitate the MLR-PPL task.
Specifically, we design a unified framework, Category-Adaptive Label Discovery
and Noise Rejection, that discovers unknown labels and rejects noisy labels for
each category in an adaptive manner. The framework consists of two
complementary modules: (1) Category-Adaptive Label Discovery module first
measures the semantic similarity between positive samples and then complement
unknown labels with high similarities; (2) Category-Adaptive Noise Rejection
module first computes the sample weights based on semantic similarities from
different samples and then discards noisy labels with low weights. Besides, we
propose a novel category-adaptive threshold updating that adaptively adjusts
the threshold, to avoid the time-consuming manual tuning process. Extensive
experiments demonstrate that our proposed method consistently outperforms
current leading algorithms.
