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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Journal ArticleDOI
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Andreas Mayr,Günter Klambauer,Thomas Unterthiner,Marvin Steijaert,Jörg K. Wegner,Hugo Ceulemans,Djork-Arné Clevert,Sepp Hochreiter +7 more
TL;DR: The to date largest comparative study of nine state-of-the-art drug target prediction methods finds that deep learning outperforms all other competitors.
Book ChapterDOI
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
TL;DR: In this article, a combination of pruning and fine-tuning is proposed to defend against backdoor attacks in deep neural networks, and it successfully weakens or even eliminates the backdoors.
Journal ArticleDOI
Video Object Segmentation without Temporal Information
Kevis-Kokitsi Maninis,Sergi Caelles,Yuhua Chen,Jordi Pont-Tuset,Laura Leal-Taixé,Daniel Cremers,L. Van Gool +6 more
TL;DR: Semantic One-Shot Video Object Segmentation is presented, based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one shot).
Proceedings ArticleDOI
MAERI: Enabling Flexible Dataflow Mapping over DNN Accelerators via Reconfigurable Interconnects
TL;DR: MAERI is a DNN accelerator built with a set of modular and configurable building blocks that can easily support myriad DNN partitions and mappings by appropriately configuring tiny switches and provides 8-459% better utilization across multiple dataflow mappings over baselines with rigid NoC fabrics.
Book ChapterDOI
Do We Really Need to Collect Millions of Faces for Effective Face Recognition
TL;DR: In this paper, the authors propose a domain specific data augmentation method to enrich an existing dataset with important facial appearance variations by manipulating the faces it contains, which is also used when matching query images represented by standard convolutional neural networks.
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.