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ImageNet Large Scale Visual Recognition Challenge

著者: Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, Li Fei-Fei - International Journal of Computer Vision, IJCV 2015 被引用: 40,207

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classifi cation and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenge of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classifi cation and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the fi ve years of the challenge, and propose future directions and improvements.

ImageNet: A large-scalehierarchical image…ImageNet: A large-scale hierarchical image databaseImageNet Classificationwith Deep Convolutional…ImageNet Classification with Deep Convolutional Neural NetworksSelective Search forObject RecognitionSelective Search for Object RecognitionDetecting Avocados toZucchinis: What Have We…Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?Microsoft COCO: CommonObjects in ContextMicrosoft COCO: Common Objects in ContextRich Feature Hierarchiesfor Accurate Object…Rich Feature Hierarchies for Accurate Object Detection and Semantic SegmentationThe Pascal Visual ObjectClasses Challenge: A…The Pascal Visual Object Classes Challenge: A RetrospectiveVisualizing andUnderstanding…Visualizing and Understanding Convolutional NetworksDeCAF: A DeepConvolutional Activatio…DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCaffe: ConvolutionalArchitecture for Fast…Caffe: Convolutional Architecture for Fast Feature EmbeddingLearning Deep Featuresfor Scene Recognition…Learning Deep Features for Scene Recognition using Places DatabaseVery Deep ConvolutionalNetworks for Large-Scal…Very Deep Convolutional Networks for Large-Scale Image RecognitionWeather classificationwith deep convolutional…Weather classification with deep convolutional neural networksTraining Object ClassDetectors with Click…Training Object Class Detectors with Click SupervisionEverything You Wanted toKnow about Deep Learnin…Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to AskA parasitic metriclearning net for breast…A parasitic metric learning net for breast mass classification based on mammographyBeyond KnowledgeDistillation…Beyond Knowledge Distillation: Collaborative Learning for Bidirectional Model AssistanceIncremental DeepLearning for Robust…Incremental Deep Learning for Robust Object Detection in Unknown Cluttered EnvironmentsMeta-Transfer Learningfor Few-Shot LearningMeta-Transfer Learning for Few-Shot LearningPerceptual QualityAssessment of Smartphon…Perceptual Quality Assessment of Smartphone PhotographyA Learning Framework forn-Bit Quantized Neural…A Learning Framework for n-Bit Quantized Neural Networks Toward FPGAsPedestrian CountingUsing Yolo V3Pedestrian Counting Using Yolo V3Crossover Learning forFast Online Video…Crossover Learning for Fast Online Video Instance SegmentationGAIA-Universe:Everything is…GAIA-Universe: Everything is Super-NetifyImageNet Large ScaleVisual Recognition…ImageNet Large Scale Visual Recognition Challenge過去の参考文献中心の論文この論文を引用する論文古い新しい

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