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
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
Heng Fan,Haibin Ling +1 more
TL;DR: C-RPN as discussed by the authors proposes a multi-stage tracking framework, which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in a Siamese network.
Proceedings ArticleDOI
DeepGauge: multi-granularity testing criteria for deep learning systems
Lei Ma,Felix Juefei-Xu,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Chunyang Chen,Ting Su,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: DeepGauge is proposed, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed and sheds light on the construction of more generic and robust DL systems.
Proceedings ArticleDOI
Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations
TL;DR: IRNet is proposed, which estimates rough areas of individual instances and detects boundaries between different object classes and enables to assign instance labels to the seeds and to propagate them within the boundaries so that the entire areas of instances can be estimated accurately.
Proceedings ArticleDOI
Deep semantic ranking based hashing for multi-label image retrieval
TL;DR: Zhang et al. as discussed by the authors proposed a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images, which avoids the limitation of semantic representation power of hand-crafted features.
Proceedings ArticleDOI
Learning to Detect Human-Object Interactions
TL;DR: Li et al. as mentioned in this paper proposed a human-object region-based convolutional neural network (HO-RCNN) for HOI detection, which is based on a novel DNN input that characterizes the spatial relations between two bounding boxes.
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.