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
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
Proceedings ArticleDOI
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
TL;DR: In this paper, a ResNet-like architecture is proposed to combine multi-scale context with pixel-level accuracy by using two processing streams within the network: one stream carries information at the full image resolution and the other stream undergoes a sequence of pooling operations to obtain robust features for recognition.
Proceedings ArticleDOI
A New Representation of Skeleton Sequences for 3D Action Recognition
TL;DR: Deep convolutional neural networks are proposed to be used to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and a Multi-Task Learning Network (MTLN) is proposed to jointly process all Frames of the clips in parallel to incorporate spatial structural information for action recognition.
Posted Content
On Evaluation of Embodied Navigation Agents
Peter Anderson,Angel X. Chang,Devendra Singh Chaplot,Alexey Dosovitskiy,Saurabh Gupta,Vladlen Koltun,Jana Kosecka,Jitendra Malik,Roozbeh Mottaghi,Manolis Savva,Amir Roshan Zamir +10 more
TL;DR: The present document summarizes the consensus recommendations of a working group to study empirical methodology in navigation research and discusses different problem statements and the role of generalization, present evaluation measures, and provides standard scenarios that can be used for benchmarking.
Posted Content
Big Transfer (BiT): General Visual Representation Learning
Alexander Kolesnikov,Lucas Beyer,Xiaohua Zhai,Joan Puigcerver,Jessica Yung,Sylvain Gelly,Neil Houlsby +6 more
TL;DR: By combining a few carefully selected components, and transferring using a simple heuristic, Big Transfer achieves strong performance on over 20 datasets and performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples.
Journal ArticleDOI
Video Super-Resolution With Convolutional Neural Networks
TL;DR: This paper proposes a CNN that is trained on both the spatial and the temporal dimensions of videos to enhance their spatial resolution and shows that by using images to pretrain the model, a relatively small video database is sufficient for the training of the model to achieve and improve upon the current state-of-the-art.
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.