Open AccessPosted Content
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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Deep Learning Based Semantic Video Indexing and Retrieval
Anna Podlesnaya,Sergey Podlesnyy +1 more
TL;DR: It is shown that deep learned features might serve as universal signature for semantic content of video useful in many search and retrieval tasks and graph-based storage structure for video index allows to efficiently retrieving the content with complicated spatial and temporal search queries.
Journal ArticleDOI
Robotic Coral Reef Health Assessment Using Automated Image Analysis
TL;DR: This paper describes the strategy to safely and effectively deploy a small marine robot to inspect a reef using its digital cameras, and demonstrates the feasibility of such a system for practical use for the preservation of this crucial ecological resource.
Journal ArticleDOI
Bird detection and species classification with time-lapse images around a wind farm: Dataset construction and evaluation
TL;DR: In this article, the authors constructed a bird image dataset that is derived from the actual environment of a wind farm and that is useful for examining realistic challenges in bird recognition in practice, which consists of high-resolution images covering a wide monitoring area around a turbine.
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Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation
Anabel Gómez-Ríos,Siham Tabik,Julián Luengo,Asm Shihavuddin,Bartosz Krawczyk,Francisco Herrera +5 more
TL;DR: The objective of this paper is to develop an accurate classification model for coral texture images and achieves the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.
Proceedings ArticleDOI
Probabilistic Label Relation Graphs with Ising Models
TL;DR: The HEX model is extended to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels, and the graph can be converted to an Ising model, which allows for existing off-the-shelf inference methods to be used.
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.