태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Weakly-supervised Video Anomaly Detection (WS-VAD) refers to identifying unusual or anomalous behaviors within video sequences using models trained primarily on video-level labels, without explicit frame-level annotations. Unlike fully-supervised methods, weakly-supervised approaches significantly reduce annotation costs by leveraging coarse labels (e.g., videos labeled as normal or anomalous). The primary challenge of this task is accurately localizing temporal anomalies and effectively distinguishing subtle anomalous activities from normal background events, relying only on limited supervision signals.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The ShanghaiTech Campus dataset has 13 scenes with complex light conditions and camera angles. It contains 130 abnormal events and over 270, 000 training frames. Moreover, both the frame-level and pixel-level ground truth of abnormal events are annotated in this dataset.
UBnormal is a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, the data set introduces abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, the data set includes disjoint sets of anomaly types in the training and test collections of videos.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.