https://github.com/FourthM/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
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Abnormal Event Detection In Video is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events
Source: Unmasking the abnormal events in video
Image: Ravanbakhsh et al
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.