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Models/SSMTL++v1

SSMTL++v1

Reported on 7 benchmarks across 1 task · 1 paper · 2 SOTA

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

Methodology7 results

Anomaly DetectiononUBnormal
RBDC· 2022-07-16
25.63
SOTA
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003
Anomaly DetectiononUBnormal
TBDC· 2022-07-16
63.53
SOTA
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003
Anomaly DetectiononShanghaiTech
RBDC· uses extra data· 2022-07-16
43.2
best: 59.21 (EVAL)
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003
Anomaly DetectiononShanghaiTech
TBDC· uses extra data· 2022-07-16
84.1
best: 89.44 (EVAL)
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003
Anomaly DetectiononCUHK Avenue
FPS· 2022-07-16
20
best: 1670 (SD-MAE)
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003
Anomaly DetectiononCUHK Avenue
RBDC· 2022-07-16
40.9
best: 68.2 (EVAL)
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003
Anomaly DetectiononCUHK Avenue
TBDC· 2022-07-16
82.1
best: 89.28 (HF2VAD+SSPCAB)
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection arXiv:2207.08003