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Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because thebinary anomaly label is only given on the video level, but the output requiressnippet-level predictions. So, Multiple Instance Learning (MIL) is prevailingin WSVAD. However, MIL is notoriously known to suffer from many false alarmsbecause the snippet-level detector is easily biased towards the abnormalsnippets with simple context, confused by the normality with the same bias, andmissing the anomaly with a different pattern. To this end, we propose a new MILframework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improveWSVAD. At each MIL training iteration, we use the current detector to dividethe samples into two groups with different context biases: the most confidentabnormal/normal snippets and the rest ambiguous ones. Then, by seeking theinvariant features across the two sample groups, we can remove the variantcontext biases. Extensive experiments on benchmarks UCF-Crime and TADdemonstrate the effectiveness of our UMIL. Our code is provided athttps://github.com/ktr-hubrt/UMIL.

Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection