scholarly journals Sparsely Aggregated Convolutional Networks

2018 ◽  
pp. 192-208 ◽  
Author(s):  
Ligeng Zhu ◽  
Ruizhi Deng ◽  
Michael Maire ◽  
Zhiwei Deng ◽  
Greg Mori ◽  
...  
Keyword(s):  
Convolutional Networks

scholarly journals Label-Aware Graph Convolutional Networks

2020 ◽  
Author(s):  
Hao Chen ◽  
Yue Xu ◽  
Feiran Huang ◽  
Zengde Deng ◽  
Wenbing Huang ◽  
...  
Keyword(s):  
Convolutional Networks

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals Detection of foraging behavior from accelerometer data using U-Net type convolutional networks

2021 ◽  
pp. 101275
Author(s):  
Mạnh Cường Ngô ◽  
Raghavendra Selvan ◽  
Outi Tervo ◽  
Mads Peter Heide-Jørgensen ◽  
Susanne Ditlevsen
Keyword(s):  
Foraging Behavior ◽  
Accelerometer Data ◽  
Convolutional Networks
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