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title
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CVL Segmentation
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CVL
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2025-12-16 18:11:37
raw text
CVL Segmentation Home Publications Team Image and Video Segmentation @ ETHZ CVL From evaluation to State-of-the-Art Results Deep Extreme Cut: From Extreme Points to Object Segmentation DEXTR 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. One-Shot Video Object Segmentation OSVOS is based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). OSVOS is very fast and improves the state of the art by a significant margin. Convolutional Oriented Boundaries COB i...
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14 - biomedicalcomputervision.uniandes.edu.co, ee.ethz.ch, vision.ee.ethz.ch, ieeexplore.ieee.org, vision.in.tum.de, graphics.ethz.ch
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