Gesture Recognition Portfolios for Personalization

34 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates (2017, methodology): “As shown in Table 8, our method surpasses the state-ofthe-art methods [39], [91], [92], [93], [94], [95], which demonstrates the effectiveness of our method in dealing with skeleton-based action recognition problem.”
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks (2017, neutral): “6 Portfolios [52] – – 56.”
Skeleton based action recognition with convolutional neural network (2015, methodology): “We follow the experimental protocol adopted in [3, 14, 22, 25] and provide precision, recall and F1-score measures on the validation set.”
Hierarchical motion evolution for action recognition (2015, methodology): “We report precision, recall, F1-score and mAP on the validation set, as done in [17, 28].”
Modeling video evolution for action recognition (2015, unknown): “[43] – – 56.”
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks (2017, background): “[32] propose a domain adaptation approach to classify user-specific human gestures.”
Rank Pooling for Action Recognition (2015, unknown): “Yao et al [73] – – 56.”