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GTC ON-DEMAND

AI & Deep Learning Research
Presentation
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Learning Affinity via Spatial Propagation Networks
Abstract:
We provide a unified framework on learning affinity in pure data-driven fashion using a linear propagation structure. This is a GPU and deep learning friendly pairwise learning module that does not require solving linear equation, iterative inferences or manually defined kernels. Specifically, we develop a three-way connection for the linear propagation model, which formulates a sparse transformation matrix, where all elements can be the output from a deep CNN, but results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix. The spatial propagation network can be applied to many affinity-related tasks, such as image matting, segmentation and colorization, to name a few. Essentially, the model can learn semantically aware affinity relations for high-level vision tasks due to the powerful learning capability of the deep CNN. We validate the framework on the task of refinement for image segmentation boundaries. Experiments on face parsing and semantic segmentation tasks show that the spatial propagation network provides a general, effective, and efficient solution for generating high-quality segmentation results.
 
Topics:
AI & Deep Learning Research, Computer Vision, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8312
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