Search results
Results from the WOW.Com Content Network
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]
Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing .
Download QR code; Print/export ... In the SqueezeNet paper, ... Semantic Segmentation of LIDAR. TensorFlow [29] SqueezeNext [30] Image
Part-of-speech tagging (which resolves some semantic ambiguity) is a related problem, and often a prerequisite for or a subproblem of syntactic parsing. Syntactic parses can be used for information extraction (e.g. event parsing, semantic role labelling, entity labelling) and may be further used to extract formal semantic representations.
The term zero-shot learning itself first appeared in the literature in a 2009 paper from Palatucci, Hinton, Pomerleau, and Mitchell at NIPS’09. [5] This terminology was repeated later in another computer vision paper [6] and the term zero-shot learning caught on, as a take-off on one-shot learning that was introduced in computer vision years ...
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
BabelNet is a multilingual lexical-semantic knowledge graph, ontology and encyclopedic dictionary developed at the NLP group of the Sapienza University of Rome under the supervision of Roberto Navigli. [1] [2] BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet.