Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- pp 248-255
TLDR
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.Abstract:
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.read more
Citations
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Proceedings ArticleDOI
SUN attribute database: Discovering, annotating, and recognizing scene attributes
Genevieve Patterson,James Hays +1 more
TL;DR: This paper performs crowd-sourced human studies to find a taxonomy of 102 discriminative attributes and builds the “SUN attribute database” on top of the diverse SUN categorical database, which has potential for use in high-level scene understanding and fine-grained scene recognition.
Book ChapterDOI
Learning to Refine Object Segments
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VL-BERT: Pre-training of Generic Visual-Linguistic Representations
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ResNeSt: Split-Attention Networks
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TL;DR: A simple and modular Split-Attention block that enables attention across feature-map groups ResNet-style is presented that preserves the overall ResNet structure to be used in downstream tasks straightforwardly without introducing additional computational costs.
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Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
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References
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Proceedings ArticleDOI
Scalable Recognition with a Vocabulary Tree
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