Generic Multi-label Annotation via Adaptive Graph and Marginalized Augmentation

Multi-label learning recovers multiple labels from a single instance. It is a more challenging task compared with single-label manner. Most multi-label learning approaches need large-scale well-labeled samples to achieve high accurate performance. However, it is expensive to build such a dataset. In this work, we propose a generic multi-label learning framework based on Adaptive Graph and Marginalized Augmentation (AGMA) in a semi-supervised scenario. Generally speaking, AGMA makes use of a small amount of labeled data associated with a lot of unlabeled data to boost the learning performance. First, an adaptive similarity graph is learned to effectively capture the intrinsic structure within the data. Second, marginalized augmentation strategy is explored to enhance the model generalization and robustness. Third, a feature-label autoencoder is further deployed to improve inferring efficiency. All the modules are jointly trained to benefit each other. State-of-the-art benchmarks in both traditional and zero-shot multi-label learning scenarios are evaluated. Experiments and ablation studies illustrate the accuracy and efficiency of our AGMA method.

Automated summary

The AGMA method is a generic multi-label learning framework based on adaptive graph and marginalized augmentation, which improves learning performance by combining a small amount of labeled data with a large amount of unlabeled data. This method utilizes adaptive similarity graphs, marginalized augmentation strategies, and feature-label autoencoders to enhance the model's generalization capability and efficiency.