5 accepted papers
Defect detection aims to detect and localize regions out of the normal distribution. The previous approaches often explicitly incorporate the defect detection concept, such as by utilizing self-supervised ground truth or manually defined feature comparison. The aforementioned processes involve model…
Data augmentation methods are commonly integrated into the training of anomaly detection models. Previous approaches have primarily focused on replicating real-world anomalies or enhancing diversity, without considering that the standard of anomaly varies across different classes, potentially leadin…
Existing feature matching methods tend to extract feature descriptors by feeding down-sampled feature maps into a Transformer that is unable to extend feature scales, leading to false correspondences between small-size objects. This paper proposes MSFormer, which uses Transformers situated in differ…
Heavy computation is a bottleneck limiting deep-learning-based feature matching algorithms to be applied in many real-time applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are ex…
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and…