The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Colorization as a Proxy Task for Visual Understanding
Abstract
We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods not using ImageNet labels for pretraining representations. Moreover, we present the first in-depth analysis of self-supervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining.
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Cites
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ImageNet Large Scale Visual Recognition Challenge
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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Multi-scale context aggregation by dilated convolutions
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Context Encoders: Feature Learning by Inpainting
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Cited by
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664
Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477
“…on other vision tasks, such as object recognition, object detection, and semantic segmentation (Zhang et al., 2016a; Larsson et al., 2016; Zhang et al., 2016b; Larsson et al., 2017; Doersch et al., 2015; Noroozi & Favaro, 2016; Noroozi et al., 2017; Pathak et al., 2016a; Doersch & Zisserman, 2017).”
BackgroundSelf-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036
Self-Supervised Learning of Pretext-Invariant Representations
Misra, I., & van der Maaten, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00674 · Cited by 1,435
Data-Efficient Image Recognition with Contrastive Predictive Coding
Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv · doi:10.48550/arxiv.1905.09272 · Cited by 1,014
Metadata
- Authors
- Larsson, G., Maire, M., & Shakhnarovich, G.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 840-849
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2599837529
- Cited by
- 458
- References
- 15
- FWCI
- 17.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Larsson, G., Maire, M., & Shakhnarovich, G. (2017). Colorization as a Proxy Task for Visual Understanding. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.96
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