Discriminative figure-centric models for joint action localization and recognition

By Tian Lan, Yang Wang, Greg Mori. 2011

246 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

End-to-End Learning of Visual Representations From Uncurated Instructional Videos (2019, methodology): “Other [22] target spatio-temporal action localization and recognition in video using a latent SVM.”
End-to-End Learning of Action Detection from Frame Glimpses in Videos (2015, background): “It must learn to effectively utilize these observations, or frame glimpses, to reason on the temporal bounds of actions.”
Towards Weakly-Supervised Action Localization (2016, methodology): “[21] consider the human position as a latent variable and infer it jointly with the action label.”
Action Classification with Locality-Constrained Linear Coding (2014, methodology): “[21] show that, with LOO, the learning method can exploit this correlation and memorize the background instead of learning the action.”
Representing Videos Using Mid-level Discriminative Patches (2013, methodology): “We build upon the recent work of Singh et al. [31] and extract “video poselets” from just action labels.”
Action Recognition by Hierarchical Mid-Level Action Elements (2015, methodology): “Moreover, we demonstrate the effectiveness of our model in real-world applications such as action recognition in large-scale untrimmed videos and action parsing.”
Video Action Detection with Relational Dynamic-Poselets (2014, methodology): “Action detection has been comprehensively studied [8,14,34,4,5,32,9,33,20,19].”
A Review of Human Activity Recognition Methods (2015, methodology): “Activity recognition and localization via a figure-centric model was presented by Lan et al. (2011).”
Action Tubelet Detector for Spatio-Temporal Action Localization (2017, methodology): “In our experiments, we use the train/test split defined in [13].”
Learning from Video and Text via Large-Scale Discriminative Clustering (2017, methodology): “Other [22] target spatio-temporal action localization and recognition in video using a latent SVM.”
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions (2017, unknown): “We employ the test split as suggested in [25].”
TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal (2017, background): “Video based Action Proposal: Early success in action detection is based on exhaustive search using sliding cuboids [13, 15, 35].”
Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video (2016, background): “Common strategies are sliding temporal window search [24]–[26] or analyzing tracked objects [27]–[30].”