通过动态路由专家提升难分类别识别效果
Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition
- 根据预测不确定性和历史表现评估类别难度，自适应调整损失权重
- 采用专家混合架构，罕见和困难类别识别准确率提升显著
- 无需中心路由器，靠专家自身置信度实现输入自适应路由
长尾视觉识别不仅受类别分布不均影响，还受各类别内在分类难度差异的挑战。单纯按频率重加权常忽略本就难以学习的类别。为此，我们提出DQRoute框架，结合难度感知优化与动态专家协作。DQRoute首先基于预测不确定性与历史表现估计类别难度，并用该信号指导自适应损失加权训练。在结构上，采用专家混合设计，每个专家专注类别分布的不同区域。推理时，专家预测通过其专属的OOD检测器生成的置信度加权，实现无需中心路由器的输入自适应路由。所有组件端到端联合训练。标准长尾基准测试表明，DQRoute显著提升性能，尤其在稀有和困难类别上，验证了难度建模与去中心化专家路由融合的有效性。
原文摘要 · Abstract (English)
Long-tailed visual recognition is challenging not only due to class imbalance but also because of varying classification difficulty across categories. Simply reweighting classes by frequency often overlooks those that are intrinsically hard to learn. To address this, we propose \textbf{DQRoute}, a modular framework that combines difficulty-aware optimization with dynamic expert collaboration. DQRoute first estimates class-wise difficulty based on prediction uncertainty and historical performance, and uses this signal to guide training with adaptive loss weighting. On the architectural side, DQRoute employs a mixture-of-experts design, where each expert specializes in a different region of the class distribution. At inference time, expert predictions are weighted by confidence scores derived from expert-specific OOD detectors, enabling input-adaptive routing without the need for a centralized router. All components are trained jointly in an end-to-end manner. Experiments on standard long-tailed benchmarks demonstrate that DQRoute significantly improves performance, particularly on rare and difficult classes, highlighting the benefit of integrating difficulty modeling with decentralized expert routing.
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