most citedDMAD: Dual Memory Bank for Real-World Anomaly Detection

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cs.CV2024

Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control

Yue Han, Junwei Zhu, Keke He +7

Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities…

cs.CV2024

SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

Qiangang Du, Jinlong Peng, Xu Chen +4

In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets based on paired data. However,…

cs.CV2024

Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection

Qiangang Du, Jinlong Peng, Changan Wang +6

Change detection aims to identify remote sense object changes by analyzing data between bitemporal image pairs. Due to the large temporal and spatial span of data collection in cha…

cs.CV20242 cited

DMAD: Dual Memory Bank for Real-World Anomaly Detection

Jianlong Hu, Xu Chen, Zhenye Gan +7

Training a unified model is considered to be more suitable for practical industrial anomaly detection scenarios due to its generalization ability and storage efficiency. However, t…

cs.CV2023

DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

Haoyang He, Jiangning Zhang, Hongxu Chen +6

Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have…

cs.CV2023

AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

Teng Hu, Jiangning Zhang, Ran Yi +5

Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. Althoug…