Tootfinder

Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang, Samira Ebrahimi Kahou, Narges Armanfard
https://arxiv.org/abs/2508.00777 https://a…

Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenge…

VertexRegen: Mesh Generation with Continuous Level of Detail
Xiang Zhang, Yawar Siddiqui, Armen Avetisyan, Chris Xie, Jakob Engel, Henry Howard-Jenkins
https://arxiv.org/abs/2508.09062

VertexRegen: Mesh Generation with Continuous Level of Detail
We introduce VertexRegen, a novel mesh generation framework that enables generation at a continuous level of detail. Existing autoregressive methods generate meshes in a partial-to-complete manner and thus intermediate steps of generation represent incomplete structures. VertexRegen takes inspiration from progressive meshes and reformulates the process as the reversal of edge collapse, i.e. vertex split, learned through a generative model. Experimental results demonstrate that VertexRegen produ…