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Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Ranked #2 on Abnormal Event Detection In Video on UBI-Fights
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
Ranked #2 on Traffic Accident Detection on A3D
Security surveillance is critical to social harmony and people's peaceful life.
Ranked #3 on Abnormal Event Detection In Video on UBI-Fights
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Ranked #1 on Semi-supervised Anomaly Detection on UBI-Fights
ANOMALY DETECTION IN SURVEILLANCE VIDEOS DATA AUGMENTATION GENERAL CLASSIFICATION SELF-SUPERVISED ANOMALY DETECTION SELF-SUPERVISED LEARNING SEMI-SUPERVISED ANOMALY DETECTION SEMI-SUPERVISED VIDEO CLASSIFICATION
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.