Object-Contextual Representations for Semantic Segmentation
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For general-purpose monocular depth estimation, where large-scale training data is available, DPT provides a performance increase of more than 28% compared to the top-performing fully-convolutional network for this task.
“For the task of general-purpose monocular depth estimation [30], where large-scale training data is available, DPT provides a performance increase of more than 28% when compared to the top-performing fully-convolutional network for this task.”
From Vision Transformers for Dense Prediction (2021) · cites this paper as [30]
Virtually all existing architectures for dense prediction are based on convolutional networks.
“Virtually all existing architectures for dense prediction are based on convolutional networks [6,31,34,42,49,50,53].”
From Vision Transformers for Dense Prediction (2021) · cites this paper as [50]
Pretraining on a billion-scale corpus boosts the performance of monocular depth prediction.
“It has been observed that this pretraining boosts the performance of monocular depth prediction [30].”
From Vision Transformers for Dense Prediction (2021) · cites this paper as [30]
General segmentation models often fail to preserve topological correctness, producing fragmented or disconnected masks.
“Accurately predicting topologically correct masks remains a difficult task for general segmentation models, which often produce fragmented or disconnected outputs.”
From rNCA: Self-Repairing Segmentation Masks (2025) · cites this paper as [4]
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Self-citationImage segmentation using deep learning: A survey
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“Architectural research on dense prediction frequently focuses on the decoder and its aggregation strategy [6, 7, 52, 55].”
BackgroundSegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
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Self-citationA Spacecraft Dataset for Detection, Segmentation and Parts Recognition
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rNCA: Self-Repairing Segmentation Masks
Silbernagel, M., Alonso, A., Petersen, J., Ibragimov, B., de Bruijne, M., & Wyburd, M. K. (2025) · arXiv · doi:10.48550/arxiv.2512.13397
- Authors
- Yuan, Y., Chen, X., & Wang, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Wang, Jingdong
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 173-190
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2976122994
- Cited by
- 1,285
- References
- 8
- Citations by year
- 2026: 85 · 2025: 149 · 2024: 200 · 2023: 298 · 2022: 270 · 2021: 237 · 2020: 44 · 2019: 1 · 2018: 1
- FWCI
- 65.47
- 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 › 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 › Medical Image Segmentation Techniques
- Keywords
- semantic segmentation, context aggregation, object-centric representation, HRNet, instance segmentation, cityscape
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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