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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 creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.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 five years of the challenge, and propose future directions and improvements.read more
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Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations.
TL;DR: This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for videodeep learning, and then noting the scarcity of research into explanations for these methods.
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What are the visual features underlying human versus machine vision
TL;DR: In this article, a web-based game called Clicktionary is introduced to identify visual features used by human observers during object recognition, and the importance maps derived from the game are consistent across participants and uncorrelated with image saliency measures.
Proceedings ArticleDOI
Toward an Automatic Evaluation of Retrieval Performance with Large Scale Image Collections
Adrian Popescu,Eleftherios Spyromitros-Xioufis,Symeon Papadopoulos,Hervé Le Borgne,Ioannis Kompatsiaris +4 more
TL;DR: This paper investigates whether it is possible to estimate retrieval performance in absence of manually created ground truth data, and produces an automatic performance estimation that is based on pre-existing user tags that exhibits strong positive correlation with the manual ones.
Proceedings ArticleDOI
Inferring Context from Pixels for Multimodal Image Classification
Manan Shah,Krishnamurthy Viswanathan,Chun-Ta Lu,Ariel Fuxman,Zhen Li,Aleksei Timofeev,Chao Jia,Chen Sun +7 more
TL;DR: It is demonstrated that if the authors predict textual information from pixels, they can subsequently use the predicted text to train models that improve overall performance, and the unique benefits of the multimodal nature of this framework are demonstrated.
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Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling.
TL;DR: It is proved that the generalization capacity of the Empirical Risk Minimization approach is preserved when plugging the resulting estimates of the $\Phi(Z'_i)$'s into the weighted empirical risk.
References
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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 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.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.