arXiv:2605.24969v1 Announce Type: new Abstract: Long-tailed recognition suffers from a persistent head--tail trade-off: improving tail performance often degrades head accuracy and can increase training instability. Despite strong empirical results from re-weighting, decoupled training, and multi-expert methods, key design choices about representation sharing between head and tail classes and supervision weighting across class groups remain largely heuristic. In this work, we propose OSDTW, a principled task-decomposition framework that partitions the original single-label recognition problem i
This paper addresses a persistent challenge in long-tailed recognition, a critical area for improving AI system robustness, reflecting ongoing research into more generalized and stable AI models.
Improving long-tailed recognition directly impacts the efficacy and reliability of AI systems, especially in real-world applications where data distributions are imbalanced, making AI more robust and trustworthy.
The proposed OSDTW framework offers a principled approach to a long-standing machine learning problem, potentially leading to more stable and accurate AI models that perform better on rare classes without degrading common ones.
- · AI researchers and developers
- · Companies deploying AI in imbalanced data environments
- · Machine learning frameworks
- · Heuristic approaches to long-tailed recognition
Immediate adoption of OSDTW or similar principled frameworks in machine learning research and development.
Improved performance and broader deployment of AI systems in complex, real-world data environments.
Enhanced trust in AI systems due to their increased robustness and fairness across data distributions.
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Read at arXiv cs.LG