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2D keypoints and segmentations for the Stanford Dogs Dataset. 2D keypoints and segmentations provided. 12,035 Labelled images 3D reconstruction/pose estimation 2020 [187] B. Biggs et al. The Oxford-IIIT Pet Dataset 37 categories of pets with roughly 200 images of each. Breed labeled, tight bounding box, foreground-background segmentation. ~ 7,400
Two-dimensional semantics has been used by David Chalmers to counter objections to the various arguments against materialism in the philosophy of mind. Specifically, Chalmers deploys two-dimensional semantics to "bridge the (gap between) epistemic and modal domains" in arguing from knowability or epistemic conceivability to what is necessary or ...
Semantic segmentation is an approach detecting, for every pixel, the belonging class. [18] For example, in a figure with many people, all the pixels belonging to persons will have the same class id and the pixels in the background will be classified as background.
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]
They developed a set of 8,869 semantic relations and 10,675 syntactic relations which they use as a benchmark to test the accuracy of a model. When assessing the quality of a vector model, a user may draw on this accuracy test which is implemented in word2vec, [ 28 ] or develop their own test set which is meaningful to the corpora which make up ...
2D maps and 3D grids from thousands of N-body and state-of-the-art hydrodynamic simulations spanning a broad range in the value of the cosmological and astrophysical parameters Each map and grid has 6 cosmological and astrophysical parameters associated to it 405,000 2D maps and 405,000 3D grids 2D maps and 3D grids Regression 2021 [222]
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.
A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3]