26 citations · 27 across the 3 of their papers we have counts for
4 papers
DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation
Hang Yao, Yansheng Fu, Ming Liu +4
Industrial anomaly detection benefits from anomaly samples, yet newly deployed products typically provide only normal images, making anomaly samples difficult to collect. Zero-shot…
Exploration of Class Center for Fine-Grained Visual Classification
Hang Yao, Qiguang Miao, Peipei Zhao +4
Different from large-scale classification tasks, fine-grained visual classification is a challenging task due to two critical problems: 1) evident intra-class variances and subtle…
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Yijun Liu, Yuan Meng, Fang Wu +7
Large language models (LLMs) have exhibited exciting progress in multiple scenarios, while the huge computational demands hinder their deployments in lots of real-world application…
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
Hang Yao, Ming Liu, Haolin Wang +4
Diffusion models have shown superior performance on unsupervised anomaly detection tasks. Since trained with normal data only, diffusion models tend to reconstruct normal counterpa…