Cited by 604Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningMedical Image Segmentation Techniques

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Exploring Cross-Image Pixel Contrast for Semantic Segmentation

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Wang, Wenguan, Zhou, Tianfei, Yu, Fisher, Dai, Jifeng, Konukoglu, Ender, Gool, Luc Van·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.00721

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Abstract

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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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
ORCID: Wang, Wenguan
Zhou, Tianfei
Yu, Fisher
Dai, Jifeng
Gool, Luc Van
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 7283-7293
DOI: 10.1109/iccv48922.2021.00721
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
Licence: other-oa
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Exploring Cross-Image Pixel Contrast for Semantic Segmentation

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