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• Novel routing method uses label hierarchies to guide capsule networks. • Capsule connections adapt based on relationships in the class hierarchy. • Agreement step enforces consistency across parent-child label levels. • Outperforms prior methods on six datasets with stronger hierarchy capture. Hierarchical multi-label classification in computer vision presents significant challenges in maintaining consistency across different levels of class granularity while capturing fine-grained visual details. This paper presents Taxonomy-aware Capsule Network (HT-CapsNet), a novel capsule network architecture that explicitly incorporates taxonomic relationships into its routing mechanism to address these challenges. Our key innovation lies in a taxonomy-aware routing algorithm that dynamically adjusts capsule connections based on known hierarchical relationships, enabling more effective learning of hierarchical features while enforcing taxonomic consistency. Extensive experiments on six benchmark datasets, including Fashion-MNIST, Marine-Tree, CIFAR-10, CIFAR-100, CUB-200-2011, and Stanford Cars, demonstrate that HT-CapsNet significantly outperforms existing methods across various hierarchical classification metrics. Notably, on CUB-200-2011, HT-CapsNet achieves absolute improvements of 10.32 % , 10.2 % , 10.3 % , and 8.55 % in hierarchical accuracy, F1-score, consistency, and exact match, respectively, compared to the best-performing baseline. On the Stanford Cars dataset, the model improves upon the best baseline by 21.69 % , 18.29 % , 37.34 % , and 19.95 % in the same metrics, demonstrating the robustness and effectiveness of our approach for complex hierarchical classification tasks.
Noor et al. (Fri,) studied this question.