awesome-transformers-for-segmentation
github.com/syeda-farhat/awesome-transformers-for-segmentation ↗Semantic segmentation is an important job in computer vision, and its applications have grown in popularity over the last decade.We grouped the publications that used various forms of segmentation in this repository. Particularly, every paper is built on a transformer.
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
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Installation instructions →What's inside
Papers
- 2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision2023
- 3D Deep Attentive U-Net with Transformer for Breast Tumor Segmentation from Automated Breast Volume Scanner2021
- 3D Vision with Transformers: A SurveySurvey Papers
- A Comprehensive Survey of Transformers for Computer VisionSurvey Papers
- Adaptive Template Transformer for Mitochondria Segmentation in Electron Microscopy Images2023
- AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation2022
Showing a sample of 214 resources. View the full list on GitHub →