Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied domain adaptation task, where we mix source target domain-mixed adaptation. However, extension of from classification segmentation (i.e., structured output) is nontrivial. This paper systematically studies impact mixup under adapti...

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce shifts in pixel level, feature and output level. However, almost all them largely neglect contextual dependency, which is generally shared across different domains, leading less-desired performance. In this paper, we propo...

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable top...

ÐAn adaptive image segmentation scheme is proposed employing the Delaunay triangulation for image splitting. The tessellation grid of the Delaunay triangulation is adapted to the semantics of the image data by combining region and edge information. To achieve robustness against imaging conditions (e.g., shading, shadows, illumination, and highlights), photometric invariant similarity measures, ...

A novel patch based adaptive diffusion method is presented for image denoising. This is done with the purpose of locally and feature adaptive diffusion and for attaining patch-wise best peak signal to noise ratio. Our framework uses over-segmentation method to segment the image in to sensible regions and then diffusion of each segment/region to obtain the near-optimal solution and iterates to a...