Shypd.aiThe largest open index of AI toolsContrastiveSeg is an open-source research tool for semantic segmentation, exploring cross-image pixel contrast. It provides an implementation of the ICCV2021 (Oral) paper, offering consistent performance improvements across diverse datasets.
Trending
About
ContrastiveSeg is an open-source project that implements the ICCV2021 (Oral) paper, "Exploring Cross-Image Pixel Contrast for Semantic Segmentation." This tool focuses on addressing the limitations of current semantic segmentation methods by incorporating "global" context, specifically rich semantic relations between pixels across different images. Inspired by unsupervised contrastive representation learning, ContrastiveSeg proposes a pixel-wise contrastive framework for fully supervised semantic segmentation. It enforces pixel embeddings of the same semantic class to be more similar than those from different classes, introducing a novel pixel-wise metric learning paradigm. The method can be integrated into existing segmentation frameworks without additional overhead during testing and has shown consistent performance improvements on datasets like Cityscapes, PASCALContext, and COCO-Stuff when used with models like DeepLabV3, HRNet, and various backbones.
Capabilities
Pricing & Plans
Open Source
Free
FAQs
Trending
