6 accepted papers
Defect synthesis, as a core technology for addressing the problem of few-shot defect classification, has been widely adopted in industrial scenarios. It helps alleviate the problem of insufficient model generalization capability owing to data scarcity by establishing a data augmentation pipeline. Re
Hyperbolic metric learning is highly effective in embedding hierarchical data structures. However, past work has predominantly focused on the conformal Poincaré model, leaving other geometries like the Klein model largely under-explored. In addition, the curved geodesics of the Poincaré model presen…
Mixture-of-Experts (MoE) architectures have become the key to scaling modern LLMs, yet little is understood about how their sparse routing dynamics respond to multilingual data. In this work, we analyze expert routing patterns using parallel multilingual datasets and present highly interpretable la…
Digital agents capable of automating complex computer tasks have attracted considerable attention. However, existing agent methods exhibit deficiencies in their generalization and specialization capabilities, especially in handling open-ended computer tasks in real-world environments. Inspired by th…
Anomaly detection is a challenging computer vision task in industrial scenario. Advancements in deep learning constantly revolutionize vision-based anomaly detection methods and considerable progress has been made in both supervised and self-supervised anomaly detection. The commonly-used pipeline i…
Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across diff…