  Novel Categories Discovery (NCD) facilitates learning from a partially
annotated label space and enables deep learning (DL) models to operate in an
open-world setting by identifying and differentiating instances of novel
classes based on the labeled data notions. One of the primary assumptions of
NCD is that the novel label space is perfectly disjoint and can be
equipartitioned, but it is rarely realized by most NCD approaches in practice.
To better align with this assumption, we propose a novel single-stage joint
optimization-based NCD method, Negative learning, Entropy, and Variance
regularization NCD (NEV-NCD). We demonstrate the efficacy of NEV-NCD in
previously unexplored NCD applications of video action recognition (VAR) with
the public UCF101 dataset and a curated in-house partial action-space annotated
multi-view video dataset. We perform a thorough ablation study by varying the
composition of final joint loss and associated hyper-parameters. During our
experiments with UCF101 and multi-view action dataset, NEV-NCD achieves ~ 83%
classification accuracy in test instances of labeled data. NEV-NCD achieves ~
70% clustering accuracy over unlabeled data outperforming both naive baselines
(by ~ 40%) and state-of-the-art pseudo-labeling-based approaches (by ~ 3.5%)
over both datasets. Further, we propose to incorporate optional view-invariant
feature learning with the multiview dataset to identify novel categories from
novel viewpoints. Our additional view-invariance constraint improves the
discriminative accuracy for both known and unknown categories by ~ 10% for
novel viewpoints.
