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Found 7 papers, 2 papers with code
Patch-wise Auto-Encoder for Visual Anomaly Detection
Anomaly detection without priors of the anomalies is challenging.
Application-Driven AI Paradigm for Human Action Recognition
Human action recognition in computer vision has been widely studied in recent years.
Data-Centric AI Paradigm Based on Application-Driven Fine-Grained Dataset Design
Deep learning has a wide range of applications in industrial scenario, but reducing false alarm (FA) remains a major difficulty.
A Waste Copper Granules Rating System Based on Machine Vision
Our rating network includes a segmentation network and a purity regression network, which respectively calculate the semantic segmentation heatmaps and purity results of the waste copper granules.
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection.
Deep Partial Multi-View Learning
Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing.
CPM-Nets: Cross Partial Multi-View Networks
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.
