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[2503.07853] Hier-COS: Making Deep Features Hierarchy-aware via Composition of Orthogonal Subspaces
Summary
The paper presents Hier-COS, a new framework for improving hierarchical classification in deep learning by addressing limitations in existing methods and evaluation metrics.
Why It Matters
This research is significant as it tackles the shortcomings of traditional classifiers that treat labels independently, which can lead to suboptimal performance in real-world applications where label hierarchies exist. By introducing Hier-COS and the HOPS evaluation metric, the authors provide a more effective approach to hierarchical classification, potentially advancing the field of computer vision and machine learning.
Key Takeaways
- Hier-COS framework improves hierarchical classification by adapting learning based on label hierarchy.
- Existing evaluation metrics fail to accurately measure hierarchical performance, necessitating the introduction of HOPS.
- Hier-COS achieves state-of-the-art results across multiple datasets while enhancing classification accuracy.
- The framework can transform frozen features from pretrained models to be hierarchy-aware.
- This research highlights the importance of considering label hierarchies in machine learning applications.
Computer Science > Computer Vision and Pattern Recognition arXiv:2503.07853 (cs) [Submitted on 10 Mar 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Hier-COS: Making Deep Features Hierarchy-aware via Composition of Orthogonal Subspaces Authors:Depanshu Sani, Saket Anand View a PDF of the paper titled Hier-COS: Making Deep Features Hierarchy-aware via Composition of Orthogonal Subspaces, by Depanshu Sani and Saket Anand View PDF HTML (experimental) Abstract:Traditional classifiers treat all labels as mutually independent, thereby considering all negative classes to be equally incorrect. This approach fails severely in many real-world scenarios, where a known semantic hierarchy defines a partial order of preferences over negative classes. While hierarchy-aware feature representations have shown promise in mitigating this problem, their performance is typically assessed using metrics like MS and AHD. In this paper, we highlight important shortcomings in existing hierarchical evaluation metrics, demonstrating that they are often incapable of measuring true hierarchical performance. Our analysis reveals that existing methods learn sub-optimal hierarchical representations, despite competitive MS and AHD scores. To counter these issues, we introduce Hier-COS, a novel framework for unified hierarchy-aware fine-grained and hierarchical multi-level classification. We show that Hier-COS is theoretically guaranteed to be consistent with the given hierarchy tree. Further...
