Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM.

Contrastive learning has been widely embraced for its notable success along with two augmentation methods— normal and strong augmentations—in skeleton action recognition. Existing methods gain performance largely by customizing normal augmentations while bypassing strong augmentations that riches in motion patterns. To make up for the blank, we propose a novel framework, called CStrCRL, acquiring view-invariant and discriminative features from strong augmentations by leveraging contrastive learning. Specifically, to avoid the fragility of skeleton data adversely affecting the model after applying strong augmentations, we use consistency learning to maximize the similarity between strongly and normally augmented views. Furthermore, we employ cross-view learning on strong and normal augmentations for eliminating uncertainty feature boundaries learned by the model. Moreover, we design a new backbone, termed GatedStrNet, for discriminating valid and invalid features contained in strong augmented views. Finally, extensive experiments on NTU 60/120 and PKUMMD II demonstrate that the proposed method bridges the performance gap between normal and strong augmentations on contrastive learning of skeleton recognition. Notably, with a single stream input, CStrCRL achieves accuracies of 78.93% and 84.04% on the NTU60 Xsub and Xview datasets. Our source code can be found at: https://github.com/RHu-main/CStrCRL.

Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are focusing on how to recover label distributions from logical labels, dubbed label enhancement (LE). Existing LE methods estimate label distributions by simply building a mapping relationship between features and label distributions under the supervision of logical labels. They typically overlook the fact that both features and logical labels are descriptions of the instance from different views. Therefore, we propose a novel method called Contrastive Label Enhancement (ConLE) which integrates features and logical labels into the unified projection space to generate high-level features by contrastive learning strategy. In this approach, features and logical labels belonging to the same sample are pulled closer, while those of different samples are projected farther away from each other in the projection space. Subsequently, we leverage the obtained high-level features to gain label distributions through a well-designed training strategy that considers the consistency of label attributes. Extensive experiments on LDL benchmark datasets demonstrate the effectiveness and superiority of our method.

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes. Since most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate the sample correlations in both the feature space and label space. Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness and superiority of our methods.