ImageNet Large Scale Visual Recognition Challenge
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 +11 more
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TLDR
The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.Abstract:
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification 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 challenges 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 classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.read more
Citations
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Ieee transactions on neural networks and learning systems
Derong Liu,Murad Abu-Khalaf,Adel M. Alimi,Charles Anderson,Aluizio Fausto,Ahmad Taher Azar,Bart Baesens,Giorgio Battistelli,Eduardo Bayro-Corrochano,Sander Bohte,Pantelis Bouboulis,Padua Braga,Cristiano Cervellera,Badong Chen,Sergio Cruces,Qionghai Dai,Steven Damelin,Daoyi Dong,El-Sayed El-Alfy,King Fahd,Saudi Arabia,David Elizondo,Maurizio Filippone,Yun Raymond Fu,Giorgio Gnecco,Haibo He,Shuiwang Ji,Preben Kidmose,Rhee Man Kil,Robert Legenstein,Hongyi Li,Zhijun Li,Jinling Liang,Juwei Lu,Wenlian Lu,Jiancheng Lv,Ana Maria Madureira,Massimo Panella,Robi Polikar,Danil Prokhorov,Manuel Roveri,Björn W. Schuller,Madhusudana Shashanka,Chunhua Shen,Igor Skrjanc,Yongduan Song,Stefano Squartini,Changyin Sun,Toshihisa Tanaka,Huajin Tang,Dacheng Tao,Peter Tino,Dianhui Wang,Michael J. Watts,Qinglai Wei,Stefan Wermter,Marco Wiering,Jonathan Wu,Shengli Xie,Dong Xu +59 more
TL;DR: Equipped with the global directional matching module and the directional appearance model learning module, DDEAL learns static cues from the labeled first frame and dynamically updates cues of the subsequent frames for object segmentation without using online fine-tuning.
Proceedings ArticleDOI
SEGCloud: Semantic Segmentation of 3D Point Clouds
TL;DR: SEGCloud as discussed by the authors combines the advantages of NNs, trilinear interpolation (TI) and fully connected CRF (FC-CRF) to obtain 3D point-level segmentation.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.