Progressively Parsing Interactional Objects for Fine Grained Action Detection

By Bingbing Ni, Xiaokang Yang, Shenghua Gao. 2016

78 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Temporal Convolutional Networks for Action Segmentation and Detection (2016, methodology): “In the literature, our task goes by the name action segmentation [7, 8, 6, 15, 29, 16, 10] or action detection [28, 20, 21, 25].”
Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction (2018, background): “[13] consolidate object detections at each step by modeling pair-wise object relationships and hence enforce the temporal object consistency in each additional step.”
Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos (2017, methodology): “It is seen that our method outperforms the recognition method proposed in [28].”
TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal (2017, methodology): “LSTM has been used for video action classification [5], image-sentence generation [12], object detection refinement [21] etc.”
Recurrent Modeling of Interaction Context for Collective Activity Recognition (2017, background): “Recently, LSTM network is also applied in fine grained action detection [25], human trajectory prediction [1], and in object recognition in the context of recurrent visual attention [3] [23] [29].”
One-Shot Action Localization by Learning Sequence Matching Network (2018, background): “Action localization Fully supervised action localization problem has been extensively studied [25, 30, 38, 49, 35, 48, 15, 50, 51, 34, 23, 14, 28, 47, 8].”
Videos as Space-Time Region Graphs (2018, background): “In the case of action recognition, a lot of effort has been made on modeling pairwise human-object and object-object relationships [55,56,57].”
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment (2018, background): “Recently, visual relations have been combined with deep neural networks in areas such as object recognition [21, 11], visual question answering [40] and action recognition [31, 34].”
Going deeper into action recognition: A survey (2016, background): “Therefore, fine-grained action recognition tasks (Ni et al., 2016; Singh et al., 2016; Lea et al., 2016) that have been already receiving growing attention from the community, can shape the future solutions and associated problems.”