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Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Open accessAbstract
Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive algorithm for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HRNet), our method brings performance improvements across diverse datasets (i.e., Cityscapes, PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our community to rethink the current de facto training paradigm in semantic segmentation.
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“Secondly, SCL can eliminate the task-independent information by constructing positive data pairs.”
From Dual Contrastive Learning for General Face Forgery Detection (2022) · cites this paper as Wang et al. 2021
In the citing paper
“Robinson et al. (2021); Wang et al. (2021) found that the hard negative pair selection is crucial for contrastive learning.”
From Dual Contrastive Learning for General Face Forgery Detection (2022) · cites this paper as Wang et al. (2021)
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Metadata
- Authors
- Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., & Gool, L. V.
- Affiliations
- ETH Zurich (CH)
- Sensetime (China) (CN)
- ETH Zurich,Computer Vision Lab
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 7283-7293
- OpenAlex
- W3122412340
- Cited by
- 604
- References
- 99
- Citations by year
- 2026: 48 · 2025: 85 · 2024: 145 · 2023: 182 · 2022: 110 · 2021: 34
- FWCI
- 38.51
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- 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, unsupervised contrastive learning, metric learning, global context, pixel embedding, training paradigms
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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