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Found 5 papers, 1 papers with code
Unsupervised Action Discovery and Localization in Videos
Once classes are discovered, training videos within each cluster are selected to perform automatic spatio-temporal annotations, by first oversegmenting videos in each discovered class into supervoxels and constructing a directed graph to apply a variant of knapsack problem with temporal constraints.
Online Localization and Prediction of Actions and Interactions
For online prediction of action (interaction) confidences, we propose an approach based on Structural SVM that operates on short video segments, and is trained with the objective that confidence of an action or interaction increases as time progresses.
Predicting the Where and What of Actors and Actions Through Online Action Localization
This paper proposes a novel approach to tackle the challenging problem of 'online action localization' which entails predicting actions and their locations as they happen in a video.
Action Localization in Videos Through Context Walk
Context relations are learned during training which capture displacements from all the supervoxels in a video to those belonging to foreground actions.
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
Ranked #5 on
Action Recognition In Videos
on UCF101
Action Recognition In Videos
Skeleton Based Action Recognition
+1
