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Found 5 papers, 1 papers with code
Few-Shot Action Localization without Knowing Boundaries
Learning to localize actions in long, cluttered, and untrimmed videos is a hard task, that in the literature has typically been addressed assuming the availability of large amounts of annotated training samples for each class -- either in a fully-supervised setting, where action boundaries are known, or in a weakly-supervised setting, where only class labels are known for each video.
Boundary Uncertainty in a Single-Stage Temporal Action Localization Network
We use two uncertainty-aware boundary regression losses: first, the Kullback-Leibler divergence between the ground truth location of the boundary and the Gaussian modeling the prediction of the boundary and second, the expectation of the $\ell_1$ loss under the same Gaussian.
Temporal Action Localization with Variance-Aware Networks
Results in the action localization problem show that the incorporation of second order statistics improves over the baseline network, and that VANp surpasses the accuracy of virtually all other two-stage networks without involving any additional parameters.
Exploring Feature Representation and Training strategies in Temporal Action Localization
Temporal action localization has recently attracted significant interest in the Computer Vision community.
hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification
Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased.
