Partial Label Learning with Semantic Label Representations

Partial-label learning (PLL) solves the problem where each training instance is assigned a candidate label set, among which only one is the ground-truth label. The core of PLL is to learn efficient feature representations to facilitate label disambiguation. However, existing PLL methods only learn plain representations by coarse supervision, which is incapable of capturing sufficiently distinguishable representations, especially when confronted with the knotty label ambiguity, i.e., certain candidate labels share similar visual patterns. In this paper, we propose a novel framework partial label learning with semantic label representations dubbed ParSE, which consists of two synergistic processes, including visual-semantic representation learning and powerful label disambiguation. In the former process, we propose a novel weighted calibration rank loss that has two implications. First, it implies a progressive calibration strategy that utilizes the disambiguated label confidence to weight the similarity between each image feature embedding and its corresponding semantic label representations of all candidates. Second, it also considers the ranking relationship between candidate and non-candidate ones. Based on learned visual-semantic representations, subsequent label disambiguation is desirably endowed with more powerful abilities. Experiments on benchmarks show that ParSE outperforms state-of-the-art counterparts.