Finally, we reconstruct the feature extractor to ensure that our model can obtain more richer and robust features.
We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China.
Novelty detection is a important research area which mainly solves the classification problem of inliers which usually consists of normal samples and outliers composed of abnormal samples.
Shape illustration images (SIIs) are common and important in describing the cross-sections of industrial products.
The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction.
Both features and the channel weights are utilized in a template generation layer to generate a discriminative template.
Due to its efficiency and stability, Robust Principal Component Analysis (RPCA) has been emerging as a promising tool for moving object detection.