Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/GCN-Anomaly

GCN-Anomaly

Reported on 6 benchmarks across 3 tasks · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision4 results

Video UnderstandingonShanghaiTech Weakly Supervised
AUC-ROC· 2019-03-18
84.44
best: 98.14 (PEL)
SOTA
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection arXiv:1903.07256
Video UnderstandingonUCSD Peds2
AUC· 2019-03-18
93.2
best: 98.7 (Background-Agnostic Framework)
SOTA
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection arXiv:1903.07256
VideoonShanghaiTech Weakly Supervised
AUC-ROC· 2019-03-18
84.44
best: 98.14 (PEL)
SOTA
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection arXiv:1903.07256
VideoonUCSD Peds2
AUC· 2019-03-18
93.2
best: 98.7 (Background-Agnostic Framework)
SOTA
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection arXiv:1903.07256

Methodology2 results

Anomaly DetectiononUCSD Peds2
AUC· 2019-03-18
93.2
best: 98.7 (Background-Agnostic Framework)
SOTA
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection arXiv:1903.07256
Anomaly DetectiononShanghaiTech Weakly Supervised
AUC-ROC· 2019-03-18
84.44
best: 98.14 (PEL)
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection arXiv:1903.07256