Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization
By Humam Alwassel, Fabian Caba Heilbron, Bernard Ghanem. 2017
85 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Bernard Ghanem
- Andrew Zisserman
- Lorenzo Torresani
- Cordelia Schmid
- Jitendra Malik
- Christoph Feichtenhofer
- Kristen Grauman
Citations in the corpus
- Dynamic Neural Networks: A Survey (2021, background): “Existing arts [168], [169], [174] typically learn to predict the location that the network should jump to at each time step.”
- Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization (2019, methodology): “Other categories of methods have also been studied, such as those based on single-shot detectors [4, 30] or sequential decision-making process [48, 2].”
- 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”
- 3D CNNs with Adaptive Temporal Feature Resolutions (2020, unknown): “There are various efforts on temporal action detection or finding action segments in untrimmed videos like [1, 23, 33, 28].”
- G-TAD: Sub-Graph Localization for Temporal Action Detection (2019, background): “In fact, empirical evidence shows that humans can reliably guess or predict the occurrence of a certain type of action by only looking at short video snippets where the action does not happen [1, 2].”
- ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos (2020, methodology): “The article closest to our work is [14] where Alwassel et al.”
- X3D: Expanding Architectures for Efficient Video Recognition (2020, result): “[1,36,52,73,74,78], which we think can be complementary to our approach.”
- SCSampler: Sampling Salient Clips From Video for Efficient Action Recognition (2019, background): “Action localization is often approached through a two-step mechanism [5, 8, 5, 14, 15, 21, 28, 1], where first an action proposal method identifies candidate action segments, and then a more sophisticated approach validates the class of each candidate and refines its temporal boundaries.”