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DIALER: A Case for Improving Rare-Class Accuracy in Retraining-Free Edge Video Analytics
Edge video analytics with lightweight models is prone to accuracy degradation due to persistent distributional shifts in live video streams. While continuous learning (CL) addresses such data drift, it heavily strains the limited compute resources of edge servers originally provisioned for inference
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