Generative Multi-Stream Architecture For American Sign Language Recognition

• URL: http://arxiv.org/abs/2003.08743v1
• Date: Mon, 9 Mar 2020 21:04:51 GMT
• Title: Generative Multi-Stream Architecture For American Sign Language Recognition
• Authors: Dom Huh, Sai Gurrapu, Frederick Olson, Huzefa Rangwala, Parth Pathak, Jana Kosecka
• Abstract summary: Training on datasets with low feature-richness for complex applications limit optimal convergence below human performance. We propose a generative multistream architecture, eliminating the need for additional hardware with the intent to improve feature convergence without risking impracticability. Our methods have achieved 95.62% validation accuracy with a variance of 1.42% from training, outperforming past models by 0.45% in validation accuracy and 5.53% in variance.
• Score: 15.717424753251674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With advancements in deep model architectures, tasks in computer vision can reach optimal convergence provided proper data preprocessing and model parameter initialization. However, training on datasets with low feature-richness for complex applications limit and detriment optimal convergence below human performance. In past works, researchers have provided external sources of complementary data at the cost of supplementary hardware, which are fed in streams to counteract this limitation and boost performance. We propose a generative multi-stream architecture, eliminating the need for additional hardware with the intent to improve feature richness without risking impracticability. We also introduce the compact spatio-temporal residual block to the standard 3-dimensional convolutional model, C3D. Our rC3D model performs comparatively to the top C3D residual variant architecture, the pseudo-3D model, on the FASL-RGB dataset. Our methods have achieved 95.62% validation accuracy with a variance of 1.42% from training, outperforming past models by 0.45% in validation accuracy and 5.53% in variance.

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