1. [Publications](/index.php/publications) 2. Learning Superpixels with Segmentation-Aware Affinity Losse # Learning Superpixels with Segmentation-Aware Affinity Losse ![](/sites/default/files/styles/wide/public/publications/overview_1.png?itok=cWQxs9m6) Superpixel segmentation has been widely used in many computer vision tasks. Existing superpixel algorithms are mainly based on hand-crafted features, which often fail to preserve weak object boundaries. In this work, we leverage deep neural networks to facilitate extracting superpixels from images. We show a simple integration of deep features with existing superpixel algorithms does not result in better performance as these features do not model segmentation. Instead, we propose a segmentation-aware affinity learning approach for superpixel segmentation. Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning. We also develop the Pixel Affinity Net for affinity prediction. Extensive experimental results show that the proposed algorithm based on the learned segmentation-aware loss performs favorably against the state-of-the-art methods. We also demonstrate the use of the learned superpixels in numerous vision applications with consistent improvements. ## Authors Wei-Chih Tu (NTU) [Ming-Yu Liu](/index.php/person/ming-yu-liu) Varun Jampani (NVIDIA) Deqing Sun (NVIDIA) Shao-Yi Chien (NTU) Ming-Hsuan Yang (University of California Merced) [Jan Kautz](/index.php/person/jan-kautz) ## Publication Date Thursday, June 21, 2018 ## Published in [CVPR](https://ieeexplore.ieee.org/document/8578164) ## Research Area [Computer Vision](/index.php/research-area/computer-vision) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .