Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models
We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.
692d31aa4397b1ec214f6953
Noam Tsfaty
692d31aa4397b1ec214f6954
Avishai Weizman
claimed_verified
2025-12-02T08:49:38.468Z
652ac7194267f8c8024dec1a
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avishai
avishai111
user
avishai111
692d31aa4397b1ec214f6955
Liav Cohen
692d31aa4397b1ec214f6956
Moshe Tshuva
692d31aa4397b1ec214f6957
Yehudit Aperstein
Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models
We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.
A dual-backbone framework using convolutional and transformer representations with top-k pooling detects anomalies in surveillance videos with 90.7% AUC on UCF-Crime.
convolutional transformer top-k pooling