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Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionThis paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in a learned embedding space: we embed pixels of the same object instance into the vicinity of each other, using a fully convolutional network trained by a modified triplet loss as the embedding model. Then the annotated pixels are set as reference and the rest ...Conference Paper -
Deep Extreme Cut: From Extreme Points to Object Segmentation
(2018)2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do so by adding an extra channel to the image in the input of a convolutional neural network (CNN), which contains a Gaussian centered in each of the extreme points. The CNN learns to transform this information into a segmentation of an object that matches those ...Conference Paper -
Iterative Deep Retinal Topology Extraction
(2018)Lecture Notes in Computer Science ~ Patch-Based Techniques in Medical ImagingConference Paper