Learning Representations for Automatic Colorization
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Auxiliary pretext tasks are primarily used to fill in missing image parts, convert images to grayscale, and predict hidden parts.
“The auxiliary pretext is primarily used to fill in missing parts of an image [6], convert it to gray scale [7], predict hidden parts, and many other tasks.”
From Self-supervised learning: A succinct review (2023) · cites this paper as [7]
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Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation
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Audio-Visual Instance Discrimination with Cross-Modal Agreement
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Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
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Deep Exemplar-Based Video Colorization
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DoveNet: Deep Image Harmonization via Domain Verification
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- Authors
- Larsson, G., Maire, M., & Shakhnarovich, G.
- Affiliations
- University of Chicago (US)
- Toyota Technological Institute at Chicago (US)
- Corresponding author
- Larsson, Gustav
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 577-593
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2308529009
- Cited by
- 946
- References
- 51
- Citations by year
- 2026: 25 · 2025: 45 · 2024: 75 · 2023: 109 · 2022: 179 · 2021: 176 · 2020: 133 · 2019: 93 · 2018: 63 · 2017: 43 · 2016: 5
- FWCI
- 23.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image colorization, automatic colorization, self-supervised visual representation learning, semantic representation
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