Unsupervised Visual Representation Learning by Context Prediction

Doersch, Carl; Gupta, Abhinav; Efros, Alexei A.

doi:10.1109/iccv.2015.167

Cited by 2,629Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Unsupervised Visual Representation Learning by Context Prediction

Doersch, Carl, Gupta, Abhinav, Efros, Alexei A.·2015·2015 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2015.167

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Abstract

This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations.

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Doersch et al. proposed using SSL to predict a missing patch by selecting a random patch and predicting the relative position of an image's second and central patches.
“Doersch et al. [18] proposed using SSL to predict a missing patch. They chose a random patch and predicted the relative position of an image's second and central patches, as shown in Fig. 9b.”
From Self-supervised learning: A succinct review (2023) · cites this paper as [18]

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Authors: Doersch, C., Gupta, A., & Efros, A. A.
Affiliations: Carnegie Mellon University (US)
University of California, Berkeley (US)
ORCID: Gupta, Abhinav
Efros, Alexei A.
Venue: 2015 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2015-12
Pages: 1422-1430
DOI: 10.1109/iccv.2015.167
OpenAlex: W343636949
Cited by: 2,629
References: 57
Citations by year: 2026: 58 · 2025: 157 · 2024: 276 · 2023: 381 · 2022: 453 · 2021: 561 · 2020: 361 · 2019: 191 · 2018: 90 · 2017: 54 · 2016: 45 · 2015: 2
FWCI: 26.11
Type: conference paper
Language: en
Open access: closed
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords: spatial context, object recognition, visual similarity, unsupervised object discovery, R-CNN, convolutional neural networks

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Unsupervised Visual Representation Learning by Context Prediction

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