The “Collection and Digital Libraries Guide” (Guia de Bibliotecas Digitais e Acervos Online, Portuguese only), published by the State Government of São Paulo, Brazil, lists 76 collections and digital libraries with free contents, including images, for consulting and download on different topics. The indicated collections are maintained by important national and international institutions, constituting a wide repository of cultural collections that can contribute to the development of research and motivate the sharing of knowledge in various areas through the internet.
Webinar abstract: It took nature and evolution more than 500 million years to develop a powerful visual system in humans. The journey for AI and computer vision is about half of a century. In this talk, Dr. Li will briefly discuss the key ideas and the cutting-edge advances in the quest for visual intelligence in computers, focusing on work done to develop ImageNet over the years.
Some highlights of this webinar:
1) The impact of ImageNet on AI/ ML research:
First. What’s ImageNet? It’s an image database, a “… largescale ontology of images built upon the backbone of the WordNet structure”;
ImageNet became a key driven-force for deep learning implementation and helped to spread the culture of building structured datasets for specific domains:
Kaggle: a platform for predictive modeling and analytics competitions in which companies and researchers post data and statisticians and data miners compete to produce the best models for predicting and describing the data
Datasets – not algorithms – might be the key limiting factor to develpment of human-level artificial inteligence.” (Alexander Wissner-Gross, 2016)
2) The background of ImageNet
The beginning: Publication about ImageNet in CVPR (2009);
There are a lot of previous datasets that should be acknowledged:
The reason why ImageNet became so popular is that this dataset has the rights characteristics to implement Computer Vision (CV) tasks from a Machine Learning (ML) approach.;
By 2005, the marriage of ML and CV became a trend in the scientific community;
There was a shift in the way ML was applied for visual recognition tasks: from a modeling-oriented approach to having lots of data.
This shift was partly enabled by the rapid internet data growth, that meant the opportunity to collect a large-scale visual data.
3) From Wordnet to ImageNet
ImageNet was built upon the backbone of the WordNet, a tremendous dataset that enabled work in Natural Language Processing (NLP) and related tasks.
What’s WordNet? It’s a large lexical database of English. The original paper (3) by George Miller et al is cited over 5k. The database organizers over 150k words into 117k categories. It establishes ontological and lexical relationships in NLP and related tasks.
The idea to move from language to image:
Three steps shift:
Step 1: ontological structures based on wordnet;
Step 2: populate categories with thousands of images from the internet;
Step3: clean bad results manually. By cleaning the errors you ensure your dataset is accurate.
Three attempts to populate, train and test the dataset. The first two failed. The third was successful due to a new technology that became available by that time: Amazon Mechanical Turk, a kind of crowdsourced engineer. Imagenet had the help of 49k workers from 167 countries (2007-2010).
After three years, ImageNet goes live in 2009 (50M images organized by 10K concept categories)
4) What they did right?
Based on ML needs, ImageNet targeted scale:
Besides, the database cared about:
image quality (high resolution to better replicate human visual acuity);
accurate annotations (to create a benchmarking dataset and advance the state of machine perception);
free of Charge (to ensure immediate application and a sense of community -> democratization)
Emphasis on Community: ILSVRC challenge is launched in 2009;
ILSVRC was inspired in PASCAL by VOC (Pattern Analysis, Statistical Modelling, and Computational Learning). From 2005-2012.
Participation and performance: the number of entries increased; classification errors (top-5) went down; the average precision for object detection went up:
5) In what ImageNet invested and still investing efforts?
Lack of details: just one category annotated per image. Object detection enabled to recognize more than one class per image (through bounding boxs);
Fine-grained recognition: recognize similar objects (class of cars, for example):
6) Expected outcomes
ImageNet became a benchmark
It meant a breakthrough in object recognition
Machine learning advanced and changed dramatically
7) Unexpected outcomes
Neural Nets became popular in academical research again
Together, with the increase of accurate and available datasets and high-performance GPUs they promoted a Deep Learning revolution:
Maximize specificity in ontological structures:
Still, relatively few works uses ontological structures;
Human comparing versus machine comparing:
7) What lies ahead
moving from object recognition to human-level understanding (from perception to cognition):
That’s the concept behind Microsoft COCO (Common Objects in Context) (5), a “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”;
More recently there is the Visual Genome (6), a dataset, a knowledge base, an ongoing effort to connect structural image concepts to language:
Visual Genome dataset was further used to advance the state-of-art in CV:
Image retrieval with scene graph;
visual questioning and answering
The future of vision intelligence relies upon the integration of perception, understanding, and action;
From now on, ImageNet ILSVRC challenge will be organized by Kaggle, a data science community that organizes competitions and makes datasets available.
(1) J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009.
(2) Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, et al. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115, no. 3 (December 2015): 211–52. doi:10.1007/s11263-015-0816-y.
(3) Miller, George A. “WordNet: A Lexical Database for English.” Communications of the ACM 38, no. 11 (1995): 39–41.
(4) Deng, Jia, Alexander C. Berg, Kai Li, and Li Fei-Fei. “What Does Classifying More than 10,000 Image Categories Tell Us?” In European Conference on Computer Vision, 71–84. Springer, 2010. https://link.springer.com/chapter/10.1007/978-3-642-15555-0_6.
(5) Lin, Tsung-Yi, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. “Microsoft COCO: Common Objects in Context.” arXiv:1405.0312 [Cs], May 1, 2014. http://arxiv.org/abs/1405.0312.
(6) Krishna, Ranjay, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, et al. “Visual Genome.” Accessed September 27, 2017. https://pdfs.semanticscholar.org/fdc2/d05c9ee932fa19df3edb9922b4f0406538a4.pdf.