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Found 4 papers, 0 papers with code
A Comprehensive Augmentation Framework for Anomaly Detection
Data augmentation methods are commonly integrated into the training of anomaly detection models.
Scene-Aware Feature Matching
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene.
Exploring the Relationship between Samples and Masks for Robust Defect Localization
Defect detection aims to detect and localize regions out of the normal distribution. Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizability. This paper proposes a one-stage framework that detects defective patterns directly without the modeling process. This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network.
ParaFormer: Parallel Attention Transformer for Efficient Feature Matching
Heavy computation is a bottleneck limiting deep-learningbased feature matching algorithms to be applied in many realtime applications.
