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ImageNet

Known as: ImageNet Large Scale Visual Recognition Challenge, ImageNet competition 
The ImageNet project is a large visual database designed for use in visual object recognition software research. As of 2016, over ten million URLs of… 
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Computer visionDatabaseDeep learningDeepDream

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Perceptually Motivated Method for Image Inpainting Comparison
The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of… 
Review
2016
Review
2016
th International Conference on Knowledge Based and Intelligent Information and Engineering Systems , KES 2016 , 5-7 September 2016 , York , United Kingdom A knowledge-based image retrieval system…
The main limitations of the existing high level image retrieval approaches concern the high dependance on an external reliable… 
2016
2016
Wildlife recognition in nature documentaries with weak supervision from subtitles and external data
2015
2015
Waseda @ TRECVID 2015: Semantic Indexing
. Waseda participated in the TRECVID 2015 Semantic Indexing (SIN) task [6]. For the SIN task, our approach used the following… 
2015
2015
Semantically-driven automatic creation of training sets for object recognition
2015
2015
An effective use of adaptive combination of visual features to retrieve image semantics from a hierarchical image database
2014
2014
A sparse kernel relevance model for automatic image annotation
In this paper, we introduce a new form of the continuous relevance model (CRM), dubbed the SKL-CRM, that adaptively selects the… 
2014
2014
A distributed approach for large-scale classifier training and image classification
2014
2014
Shared Roots: Regularizing Deep Neural Networks through Multitask Learning
In this paper 
2012
2012
Appendix: Building high-level features using large scale unsupervised learning
In this appendix, we discuss more details regarding the algorithm, its implementation, test set for 3D-transformed faces… 
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