Missing labels are common in multi-label learning and can bias both label-correlation estimation and classifier induction. Existing missing-label methods often recover incomplete supervision mainly through global label correlations. However, correlation-driven recovery alone may produce over-smoothed supervision when annotations are sparse, while label-specific discriminative evidence may be weakened. To address this problem, we propose GLCS, a graph-regularized low-rank correlation learning framework with label-specific features for multi-label learning with missing labels. GLCS first uses the observed entries as reliable supervision sources and propagates them through a learned label correlation matrix. It then jointly learns sparse label-specific predictors, low-rank label correlations, and a label graph regularizer induced by the learned correlations. In this way, global label dependencies, local label-structure consistency, and label-wise discriminative features are optimized in a unified objective. The resulting problem is solved by an alternating proximal optimization scheme with soft thresholding for sparse predictors and singular value thresholding for low-rank correlations. Experiments on twelve benchmark datasets under three missing-label ratios show that GLCS obtains strong average performance across AP, AUC, CV, HL, OE, and RL, especially under high missing rates.
Tian-Lin Li, M. F. Nasrudin, Xing-Lin Peng et al.· Big Data and Cognitive Compu...· 0 citations
Face recognition has been widely applied in identity authentication systems, but its vulnerability to various presentation attacks poses significant security risks. As a result, face anti-spoofing (FAS) has become one of the key technologies for ensuring the reliability of such systems. Most existing domain- generalized FAS (DGFAS) approaches rely on domain distribution alignment to learn cross-domain invariant representations. However, many of these methods independently model each sample while overlooking semantic consistency among cross-domain samples and different regions within each sample, making them vulnerable to semantic drift. Moreover, some existing methods introduce additional pixel-level supervision signals, such as pseudo-depth maps and binary masks, which increase annotation costs and limit their generalizability. To address these challenges, we propose DSCM-FAS, a novel framework that combines inter-sample and intra-sample semantic consistency to enhance robustness and cross-domain generalization, without requiring auxiliary supervision. Specifically, we design a Dual Semantic Consistency Module (DSCM). Across samples, contrastive learning is leveraged to learn discriminative representations, followed by the adaptive construction of a high-confidence similarity adjacency graph via statistical thresholding. The resulting graph is then fed into a graph convolutional network (GCN) to strengthen cross-domain semantic consistency, thereby improving the model's robustness to domain shifts. Within each sample, the intermediate embedding features are first partitioned into multiple patches, and Laplacian Regularization is introduced to constrain the semantic relationships among different patches. This effectively suppresses local noise interference and promotes the learning of more robust and semantically consistent feature representations. Extensive experiments on four public FAS datasets demonstrate that DSCM-FAS consistently outperforms state-of-the-art methods under various protocols. These results validate the effectiveness of our DSCM-FAS in improving the cross-domain generalization of FAS models.
Hui Xing, Siok Yee Tan, Faizan Qamar et al.· Journal of King Saud Univers...· 0 citations
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