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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Proceedings ArticleDOI
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
Naveen Suda,Vikas Chandra,Ganesh Dasika,Abinash Mohanty,Yufei Ma,Sarma Vrudhula,Jae-sun Seo,Yu Cao +7 more
TL;DR: This work presents a systematic design space exploration methodology to maximize the throughput of an OpenCL-based FPGA accelerator for a given CNN model, considering the FPGAs resource constraints such as on-chip memory, registers, computational resources and external memory bandwidth.
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Going Deeper in Spiking Neural Networks: VGG and Residual Architectures.
TL;DR: In this paper, the authors propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet.
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High-Resolution Representations for Labeling Pixels and Regions
Ke Sun,Yang Zhao,Borui Jiang,Tianheng Cheng,Bin Xiao,Dong Liu,Yadong Mu,Xinggang Wang,Wenyu Liu,Jingdong Wang +9 more
TL;DR: A simple modification is introduced to augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from thehigh-resolution convolution, which leads to stronger representations, evidenced by superior results.
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Cascade R-CNN: High Quality Object Detection and Instance Segmentation
Zhaowei Cai,Nuno Vasconcelos +1 more
TL;DR: A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, which significantly improves high-quality detection on generic and specific object datasets, including VOC, KITTI, CityPerson, and WiderFace.
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DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
TL;DR: This work shows that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs), while rather than using the networks as a blackbox feature extractor, it customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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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
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