发表机构
ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海科技大学; 上海智能视觉与成像工程技术研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究实时类别发现问题，提出基于在线狄利克雷过程高斯混合模型的DP-BOA框架，通过比较后验预测证据决定样本归属并在线更新统计，在标准OCD基准测试中性能优异，新类别发现能力强且已知类精度有竞争力。
AI 中文摘要
实时类别发现需要为每个传入的测试样本决定是将其分配到现有类别还是生成一个新类别。现有方法通常通过基于匹配的启发式方法来实现这一决策，如基于半径或哈希的规则。虽然这些方法在实践中有效，但通常将类别生成隐含地视为在没有现有类别能可靠匹配时的后备方案，而非由自身统计证据支持的明确选择。为解决此问题，我们提出DP-BOA，这是一个基于具有正态逆威沙特先验的在线狄利克雷过程高斯混合模型的后验预测决策框架。在训练期间，我们使用标记数据校准类别高斯上的共享NIW先验并热启动已知类别的后验。在测试时，对于每个传入样本，DP-BOA将分配到现有类别的后验预测证据与由DP先验诱导的生成新类别的证据进行比较，然后在决策后在线更新类别统计信息。该方法捕获各向异性类别几何结构并随着证据积累自然地调整决策置信度。在标准OCD基准测试中，DP-BOA始终优于强大的基线，在保持有竞争力的已知类精度的同时，提供特别强大的新类别发现性能。
英文摘要
On-the-fly category discovery requires deciding for each incoming test sample whether to assign it to an existing category or spawn a new one. Existing methods typically implement this decision through matching-based heuristics, such as radius- or hash-based rules. While effective in practice, these methods usually treat category birth implicitly as a fallback when no existing category matches confidently, rather than as an explicit alternative supported by its own statistical evidence. To address this, we propose DP-BOA, a posterior-predictive decision framework based on an online Dirichlet-process Gaussian mixture model with a Normal-Inverse-Wishart prior. During training, we use labeled data to calibrate a shared NIW prior over category Gaussians and warm-start the known-category posteriors. At test time, for each incoming sample, DP-BOA compares the posterior predictive evidence for assignment to existing categories against the evidence for spawning a new category induced by the DP prior, and then updates category statistics online after the decision. The method captures anisotropic category geometry and naturally adapts decision confidence as evidence accumulates. Across standard OCD benchmarks, DP-BOA consistently outperforms strong baselines and delivers particularly strong novel-class discovery performance while maintaining competitive known-class accuracy.
CommentsAccepted at ECCV 2026
