arXiv:1811.07793
DeepIR: A Deep Semantics Driven Framework for Image Retargeting
원문 초록
We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of input image with a deep convolutional neural network. Then a uniform re-sampling that suits for semantic structure preserving is devised to resize feature maps to target aspect ratio at each feature layer. The final retargeting result is generated by coarse-to-fine nearest neighbor field search and step-by-step nearest neighbor field fusion. We empirically demonstrate the effectiveness of our model with both qualitative and quantitative results on widely used RetargetMe dataset.
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Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment
Self-Play Reinforcement Learning for Fast Image Retargeting
Cycle-IR: Deep Cyclic Image Retargeting
HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
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