Predicting Actions from Static Scenes

By Tuan-Hung Vu, Catherine Olsson, Ivan Laptev, Aude Oliva, Josef Sivic. 2014

66 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “Predicting future actions before they are actually executed is a critical ingredient for enabling us to effectively interact with other humans on a daily basis (Hoai and De la Torre, 2014; Lan et al., 2014; Ryoo, 2011; Sadegh Aliakbarian et al., 2017; Vu et al., 2014).”
Anticipating the future by watching unlabeled video (2015, background): “in [44] learn scene affordance to predict what actions can happen in a static scene.”
Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016, unknown): “[55, 8, 71, 67, 44, 58] forecast actions in streaming videos.”
What do 15,000 object categories tell us about classifying and localizing actions? (2015, unknown): “The type of scene puts a prior on the possible actions [27, 49, 56] and vice-versa, an action may disambiguate similar looking objects [10, 42].”
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition (2019, background): “Leveraging scene context is useful for object detection [40, 7, 11, 13], semantic segmentation [35, 39, 40, 69], predicting invisible things [32], and action recognition without looking at the human [23, 54].”
Egocentric Future Localization (2016, background): “[38] predicted plausible activities from a static scene by associating the scene statistics and labeled actions.”
Anticipating Visual Representations from Unlabeled Video (2015, background): “Vu et al. in [38] learn scene affordance to predict what actions can happen in a static scene.”