Open AccessPosted Content
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 creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.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 five years of the challenge, and propose future directions and improvements.read more
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Deep Metric Learning using Similarities from Nonlinear Rank Approximations
TL;DR: In this paper, a nonlinear transfer function was proposed to better contract similar and disperse dissimilar samples. But, the similarity search for images is performed by determining the vectors with the smallest distances to a query vector, not the actual distances of the feature vectors, but rather on the ranking order of feature vectors from similar images.
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Optimal Low Rank Tensor Factorization for Deep Learning
TL;DR: Based on tensor rank effect observation, a novel decomposition method is proposed which preserves the underlying model’s accuracy along with time and space optimization by tensor methods.
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Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band images
Oliver Müller,Eva Schnider +1 more
TL;DR: Tanoglidis et al. as mentioned in this paper used a neural network architecture to detect low-surface brightness objects in the Dark Energy Survey (DES) and achieved an accuracy of 85% for spiral galaxies, 94% for dwarf ellipticals, and 52% for irregular galaxies.
Journal ArticleDOI
Tree-structured multilayer neural network for classification
TL;DR: In this study, the tree-structured multilayer neural network (TSMLNN) is proposed for classification and the genetic algorithm is proposed to automatically search for the weights, activation threshold of each node and the proper number of nodes at each layer according to both the computing complexity and classification error rate in the TSMLNN.
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OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
TL;DR: In this paper, the authors constructed an origami dataset using images from the multimedia commons and other databases, which consists of two subsets: one for classification of origami images and the other for difficulty estimation.
References
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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 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.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.