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1,761 claims from 1,082 papers are on the record. 46 have been checked so far; the other 1,715 have no check with a result yet.

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Keyword: SIFT flow ×Clear all

1 claim from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Fully Convolutional Networks for Semantic Segmentation

   Jonathan, Shelhamer and Darrell · arXiv (Cornell University) · 2014

   The paper shows that convolutional networks trained end-to-end, pixels-to-pixels, can exceed the state of the art in semantic segmentation, by adapting image classification networks into fully convolutional ones.

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   1. UncheckedThe authors report that their fully convolutional network beat previous best results on three segmentation benchmarks, taking about a third of a second per typical image.“Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.”