ImageNet expands the data frontier

A large, organized image database created new opportunities for visual recognition research.

Jia Deng and colleagues

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The contribution

ImageNet organized images using a semantic hierarchy and supplied labeled examples at a scale intended to support broad object recognition. The paper established the database as research infrastructure, distinct from the later annual challenge.

What this does not establish

A dataset is not a model. Benchmark performance measures performance under a particular dataset and evaluation protocol.

Why this date?

The database paper appeared at CVPR 2009. This is distinct from later ImageNet challenge results.

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The original work

ImageNet: A Large-Scale Hierarchical Image Database

Authors: Jia Deng and colleagues
Publication: IEEE Conference on Computer Vision and Pattern Recognition
Source type: Conference paper

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Cited By

Atlas entries whose reference lists cite this work or the publication version noted below.

ImageNet Classification with Deep Convolutional Neural Networks (2012)
Listed in the source bibliography. Check the reference list.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
Listed in the source bibliography. Check the reference list.
High-Resolution Image Synthesis with Latent Diffusion Models (2021)
Listed in the source bibliography. Check the reference list.
Learning Transferable Visual Models From Natural Language Supervision (2021)
Listed in the source bibliography. Check the reference list.

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Reference section 1 (PDF pages 8, 9)

[1] http://www.hunch.net/˜jl/. [2] The Chinese WordNet. http://bow.sinica.edu.tw. [3] The Spanish WordNet. http://www.lsi.upc.edu/˜nlp. [4] A. Artale, B. Magnini, and S. C. Wordnet for italian and its use for lexical discrimination. In AI*IA97, pages 16–19, 1997. [5] O. Boiman, E. Shechtman, and M. Irani. In defense of nearest- neighbor based image classiﬁcation. In CVPR08, pages 1–8, 2008. [6] B. Collins, J. Deng, K. Li, and L. Fei-Fei. Towards scalable dataset construction: An active learning approach. In ECCV08, pages I: 86– 98, 2008. [7] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results. http://www.pascal-network.org/ challenges/VOC/voc2008/workshop/. [8] L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. PAMI, 28(4):594–611, April 2006. [9] C. Fellbaum. WordNet: An Electronic Lexical Database. Bradford Books, 1998. [10] R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google’s image search. In ICCV05, pages II: 1816– 1823, 2005. [11] M. Fink and S. Ullman. From aardvark to zorro: A benchmark for mammal image classiﬁcation. IJCV, 77(1-3):143–156, May 2008. [12] G. Grifﬁn, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, Caltech, 2007. [13] G. Huang, M. Ramesh, T. Berg, and E. Learned Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, UMass, 2007. [14] L.-J. Li, G. Wang, and L. Fei-Fei. OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning. In CVPR07, pages 1–8, 2007. [15] D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91–110, November 2004. [16] M. Marszalek and C. Schmid. Semantic hierarchies for visual object recognition. In CVPR07, pages 1–7, 2007. [17] M. Marszalek and C. Schmid. Constructing category hierarchies for visual recognition. In ECCV08, pages IV: 479–491, 2008. [18] D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR06, pages II: 2161–2168, 2006. [19] P. Phillips, H. Wechsler, J. Huang, and P. Rauss. The feret database and evaluation procedure for face-recognition algorithms. IVC, 16(5):295–306, April 1998. [20] E. Rosch and B. Lloyd. Principles of categorization. In Cognition and categorization, pages 27–48, 1978. [21] B. Russell, A. Torralba, K. Murphy, and W. Freeman. Labelme: A database and web-based tool for image annotation. IJCV, 77(13):157–173, May 2008. [22] J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In ECCV06, pages I: 1–15, 2006. [23] A. Sorokin and D. Forsyth. Utility data annotation with amazon mechanical turk. In InterNet08, pages 1–8, 2008. [24] A. Torralba, R. Fergus, and W. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. PAMI, 30(11):1958–1970, November 2008. [25] L. von Ahn and L. Dabbish. Labeling images with a computer game. In CHI04, pages 319–326, 2004. [26] P. Vossen, K. Hofmann, M. de Rijke, E. Tjong Kim Sang, and K. Deschacht. The Cornetto database: Architecture and user-scenarios. In Proceedings DIR 2007, pages 89–96, 2007. [27] B. Yao, X. Yang, and S. Zhu. Introduction to a large-scale general purpose ground truth database: Methodology, annotation tool and benchmarks. In EMMCVPR07, pages 169–183, 2007. [28] A. Zweig and D. Weinshall. Exploiting object hierarchy: Combining models from different category levels. In ICCV07, pages 1–8, 2007.

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The contribution

What this does not establish

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