Sign Language Recognition using 3D convolutional neural networks

By Jie Huang, Wen-gang Zhou, Houqiang Li, Weiping Li. IEEE International Conference on Multimedia and Expo, 2015

311 citations recorded by the source corpus.

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MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language (2018, unknown): “Koller et al . showed large improvements embedding 2D-CNNs in HMMs [38, 40], related works with 3D-CNNs exist [6, 32] and weakly supervised multi-stream modeling is presented in [41].”
A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences (2017, background): “[41] proposes a 3D CNN for sign language recognition.”
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison (2019, background): “Instead of encoding the spatial and temporal information separately, recent works also employ 3D CNNs [28, 76] to capture spatial-temporal features together.”
Video-based Sign Language Recognition without Temporal Segmentation (2018, background): “There are primarily two categories: the hand-crafted features (Sun et al. 2013; Koller, Forster, and Ney 2015) and Convolutional Neural Network (CNN) based features (Tang et al. 2015; Huang et al. 2015; Pu, Zhou, and Li 2016).”