DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery

On-the-fly category discovery requires deciding for each in-coming test sample whether to assign it to an existing category or spawna new one. Existing methods typically implement this decision throughmatching-based heuristics, such as radius- or hash-based rules. While ef-fective in practice, these methods usually treat category birth implicitlyas a fallback when no existing category matches confidently, rather thanas an explicit alternative supported by its own statistical evidence. Toaddress this, we propose DP-BOA, a posterior-predictive decision frame-work based on an online Dirichlet-process Gaussian mixture model witha Normal–Inverse–Wishart prior. During training, we use labeled datato calibrate a shared NIW prior over category Gaussians and warm-startthe known-category posteriors. At test time, for each incoming sample,DP-BOA compares the posterior predictive evidence for assignment toexisting categories against the evidence for spawning a new category in-duced by the DP prior, and then updates category statistics online afterthe decision. The method captures anisotropic category geometry andnaturally adapts decision confidence as evidence accumulates. Acrossstandard OCD benchmarks, DP-BOA consistently outperforms strongbaselines and delivers particularly strong novel-class discovery perfor-mance while maintaining competitive known-class accuracy. The projectpage is available at DP-BOA.