Attribute learning in large-scale datasets
We consider the task of learning visual connections between object categories using the ImageNet dataset which is a large-scale dataset ontology containing
Learning Deep Representation with Large-scale Attributes - Wanli
contains rich attribute annotations (over 300 attributes) for ?180k samples and 494 object classes. Based on the. ImageNet object detection dataset
A Large-Scale Attribute Dataset for Zero-Shot Learning
(1) The categories and images of these datasets may be highly related to ImageNet dataset. (used in ILSVRC 2010/2012). In ZSL scenario it is thus less
Learning Multifunctional Binary Codes for Both Category and
Both Category and Attribute Oriented Retrieval Tasks This document gives details about the attributes defined on ImageNet-150K and additional real ...
Attribute learning in large-scale datasets
Abstract. We consider the task of learning visual connections between object categories using the ImageNet dataset which is a large-scale.
On the Relationship Between Visual Attributes and Convolutional
image-level object recognition on ImageNet can reliably predict absolute visual attributes [3]. Through rigorous ex- perimentation we uncover the following
Auditing ImageNet: Towards a Model-driven Framework for
4 juin 2019 Demographic Attributes of Large-Scale Image Datasets. Chris Dulhanty ... 2012 ImageNet Large Scale Visual Recognition Challenge.
Unsupervised Learning of Discriminative Attributes and Visual
For pre-training on ImageNet we particulary ini- tialize our first clustering step with the more elaborated fea- tures (SIFT+color Fisher Vectors) in [36] in
Inferring Analogous Attributes
datasets SUN [17] and ImageNet [19]
Complementary Attributes: A New Clue to Zero-Shot Learning
ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and
arXiv:190501347v2 [csLG] 4 Jun 2019
ImageNetparticularlyimportantgivenit is frequentlyused to pretrain models for a wide variety of computer vision tasks In this work we introduce a model-driven frame-workfortheautomaticannotationofapparentageandgen-der attributes in large-scale image datasets Using this framework we conduct the ?rst demographic audit of the
ImageNet Training in Minutes - University of California Berkeley
trained with the ImageNet-1k dataset while preserving the state-of-the-art test accuracy Compared to the baseline of a previous study from a group of researchers at Facebook our approach shows higher test accuracy on batch sizes that are larger than 16K Using 2048 Intel Xeon Platinum 8160 processors we reduce the 100-epoch
ImageNet MPEG-7 Visual Descriptors - arXivorg
ImageNet is organised according to the well-knownWordNet [2] ontology It is a “large lexical database ofEnglish Nouns verbs adjectives and adverbs are groupedinto sets of cognitive synonyms (synsets) each expressing adistinct concept” Hence ImageNet can be viewed as a treewhere each node corresponds to a synset
ImageNet: A Large-Scale Hierarchical Image Database
Properties of ImageNet ImageNet is built upon the hierarchical structure pro-vided by WordNet In its completion ImageNet aims tocontain in the order of 50million cleanly labeled full reso-lution images (500-1000per synset) At the time this paperis written ImageNet consists of12subtrees
Searches related to imagenet attributes filetype:pdf
ImageNet [2] is a large collection of images labeled againstWordNet 3 0 and described at http://image-net org/ Accordingto January statistics ImageNet contains 14197122 images and21841 indexed synsets There are various labeling schemes The object attributes scheme labels 400 synsets across 25 at-tributes
How many images are in ImageNet?
- sion of ImageNet with 10,184 categories and 8.9 million images. On this dataset we follow the convention in the litera- ture of using half of the images for training and half for test-
What is ImageNet large-scale visual recognition challenge (ILSVRC)?
- competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images.
Who is the author of ImageNet classification?
- ImageNet Classification with Deep DOI:10.1145/3065386 Convolutional Neural Networks By Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton
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