Unbiased multiple instance learning for weakly supervised video anomaly detection
Abstract Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the
binary anomaly label is only given on the video level, but the output requires snippet-level
predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is
notoriously known to suffer from many false alarms because the snippet-level detector is
easily biased towards the abnormal snippets with simple context, confused by the normality
with the same bias, and missing the anomaly with a different pattern. To this end, we …
binary anomaly label is only given on the video level, but the output requires snippet-level
predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is
notoriously known to suffer from many false alarms because the snippet-level detector is
easily biased towards the abnormal snippets with simple context, confused by the normality
with the same bias, and missing the anomaly with a different pattern. To this end, we …
[PDF][PDF] Unbiased multiple instance learning for weakly supervised video anomaly detection.(2023)
Abstract Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the
binary anomaly label is only given on the video level, but the output requires snippetlevel
predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is
notoriously known to suffer from many false alarms because the snippet-level detector is
easily biased towards the abnormal snippets with simple context, confused by the normality
with the same bias, and missing the anomaly with a different pattern. To this end, we …
binary anomaly label is only given on the video level, but the output requires snippetlevel
predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is
notoriously known to suffer from many false alarms because the snippet-level detector is
easily biased towards the abnormal snippets with simple context, confused by the normality
with the same bias, and missing the anomaly with a different pattern. To this end, we …
以上显示的是最相近的搜索结果。 查看全部搜索结果