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
Reads0
Chats0
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
More filters
Posted Content
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
Zhaowei Cai,Nuno Vasconcelos +1 more
TL;DR: Cascade R-CNN as mentioned in this paper is a multi-stage RNN architecture composed of a sequence of detectors trained with increasing intersection over union (IoU) thresholds, which progressively improves hypotheses quality.
Journal ArticleDOI
Microscopy cell counting and detection with fully convolutional regression networks
TL;DR: A new state-of-the-art performance for cell count on standard synthetic image benchmarks is set and it is shown that the FCRNs trained entirely with synthetic data can generalise well to real microscopy images both for cell counting and detections for the case of overlapping cells.
Proceedings ArticleDOI
Improving Deep Learning with Generic Data Augmentation
Luke Taylor,Geoff Nitschke +1 more
TL;DR: In this paper, various geometric and photometric data augmentation methods are evaluated on a coarse-grained data set using a relatively simple CNN and the results indicate that croppingin geometric augmentations significantly increases CNN task performance.
Proceedings ArticleDOI
A Variational U-Net for Conditional Appearance and Shape Generation
Patrick Esser,Ekaterina Sutter +1 more
TL;DR: In this paper, a conditional U-Net was proposed for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance, without requiring samples of the same object with varying pose or appearance.
Posted Content
A study of the effect of JPG compression on adversarial images.
TL;DR: It is found that JPG compression often reverses the drop in classification accuracy to a large extent, but not always, and as the magnitude of the perturbations increases, JPG recompression alone is insufficient to reverse the effect.
References
More filters
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.