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Found 5 papers, 2 papers with code
Neural Style Transfer via Meta Networks
Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network parameters by enormous iterations of stochastic gradient descent, which lacks the generalization ability to new style in the inference stage.
Meta Networks for Neural Style Transfer
Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network parameters by enormous iterations of stochastic gradient descent.
Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF
The proposed joint model also employs a guidance CRF to further enhance the segmentation performance.
Weighted Residuals for Very Deep Networks
The weighted residual network is able to learn to combine residuals from different layers effectively and efficiently.
Fast Semantic Image Segmentation with High Order Context and Guided Filtering
This paper describes a fast and accurate semantic image segmentation approach that encodes not only the discriminative features from deep neural networks, but also the high-order context compatibility among adjacent objects as well as low level image features.
