1 citations · 1 across the 4 of their papers we have counts for
7 papers
Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
Yaoxuan Feng, Yuxin Li, Weijiang Lv +5
Multi-class anomaly detection aims to build unified models across diverse product categories. However, as the number of categories grows, its performance often degrades due to incr…
One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection
Boan Zhang, Wen Li, Guanhua Yu +3
Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under the common assumption that of…
SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models
Weijiang Lv, Yaoxuan Feng, Xiaobo Xia +4
Chain-of-Thought reasoning is widely used to improve the interpretability of multimodal large language models (MLLMs), yet the faithfulness of the generated reasoning traces remain…
Semantically Guided Dynamic Visual Prototype Refinement for Compositional Zero-Shot Learning
Zhong Peng, Yishi Xu, Gerong Wang +4
Compositional Zero-Shot Learning (CZSL) seeks to recognize unseen state-object pairs by recombining primitives learned from seen compositions. Despite recent progress with vision-l…
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
Long Tian, Yufei Li, Yuyang Dai +3
Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approa…
Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series
Muyao Wang, Wenchao Chen, Zhibin Duan +1
Forecasting Multivariate Time Series (MTS) involves significant challenges in various application domains. One immediate challenge is modeling temporal patterns with the finite len…