What Do I Annotate Next? An Empirical Study of Active Learning for Action Localization

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How researchers see this paper

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Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, methodology): “have been used for novel tasks, through pre-training [10, 11], self-supervision [12,13] or additional annotations [14, 15].”
Diagnosing Error in Temporal Action Detectors (2018, background): “Currently, researchers have appealing intuitions to tackle video action localization problem [1,10,34,45], they are equipped with large datasets to validate their hypothesis [4,19,5], and they have access to appropriate computational power.”
Weakly-Supervised Action Localization With Background Modeling (2019, background): “videolevel class labels. Such a formulation of action understanding is attractive because it is well-known that precisely estimating the start and end frames of actions is challenging even for humans [3]. We build on a body of work that makes use of attentional processing to infer frames most likely”
FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding (2020, methodology): “More challenging datasets are proposed subsequently, including UCF101 [40], Kinetics [3], ActivityNet [11], Moments in Time [32], and others [25, 18, 50, 2, 52, 38, 33, 22, 46, 31, 20].”