This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label correlations. Specifically, their effectiveness has been proven not only when considering a single domain but also when taking into account multiple domains. However, the topology of the used graph is not optimal as it is pre-defined heuristically. In addition, consecutive Graph Convolutional Network (GCN) aggregations tend to destroy the feature similarity. To overcome these issues, an architecture for learning the graph connectivity in an end-to-end fashion is introduced. This is done by integrating an attention-based mechanism and a similarity-preserving strategy. The proposed framework is then extended to multiple domains using an adversarial training scheme. Numerous experiments are reported on well-known single-domain and multi-domain benchmarks. The results demonstrate that our approach achieves competitive results in terms of mean Average Precision (mAP) and model size as compared to the state-of-the-art. The code will be made publicly available.

翻译：本文提出了一种基于自适应图的多标签图像分类方法。图方法因其建模标签相关性的能力，已在多标签分类领域得到广泛应用。具体而言，其有效性不仅体现在单域场景中，在多域场景下同样得到验证。然而，现有方法使用的图拓扑结构因采用启发式预定义方式而并非最优，且连续的图卷积网络聚合操作会破坏特征相似性。为解决这些问题，本文引入了一种端到端学习图连通性的架构，通过融合注意力机制和相似性保持策略实现。该框架随后通过对抗训练方案扩展至多域场景。我们在著名的单域和多域基准数据集上进行了大量实验，结果表明，与现有方法相比，我们的方法在平均精度均值和模型规模方面均取得了具有竞争力的表现。相关代码将公开发布。