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Novelty detection is crucial for real-life applications. While it is commonin activity recognition to assume a closed-set setting, i.e. test samples arealways of training categories, this assumption is impractical in a real-worldscenario. Test samples can be of various categories including those never seenbefore during training. Thus, being able to know what we know and what we donot know is decisive for the model to avoid what can be catastrophicconsequences. We present in this work a novel approach for identifying samplesof activity classes that are not previously seen by the classifier. Our modelemploys a voting-based scheme that leverages the estimated uncertainty of theindividual classifiers in their predictions to measure the novelty of a newinput sample. Furthermore, the voting is privileged to a subset of informedclassifiers that can best estimate whether a sample is novel or not when it isclassified to a certain known category. In a thorough evaluation on UCF-101 andHMDB-51, we show that our model consistently outperforms state-of-the-art innovelty detection. Additionally, by combining our model with off-the-shelfzero-shot learning (ZSL) approaches, our model leads to a significantimprovement in action classification accuracy for the generalized ZSL setting.

Informed Democracy: Voting-based Novelty Detection for Action Recognition