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:fire: [ICLR2023, ECCV2022, TPAMI2024] Powerful Multi-Task Transformers for Scene Understanding
:scroll: Introduction
This repository provides codes and models for two powerful multi-task transformer models for scene understanding. Please check the following pages for details:
<p align="center"> <img alt="img-name" src="https://user-images.githubusercontent.com/14089338/232197965-8936504d-8ce4-450b-a281-069f5c2c8205.gif" width="900"> </p>Hanrong Ye and Dan Xu, TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding. ICLR 2023
<p align="center"> <img alt="img-name" src="https://user-images.githubusercontent.com/14089338/220043972-b3bfcc0d-d76e-4d34-8b20-d7c5d9f00a9f.gif" width="900"> <img alt="img-name" src="https://user-images.githubusercontent.com/14089338/220043986-291797a8-8994-4a54-846e-057e3778a972.gif" width="900"> </p>Hanrong Ye and Dan Xu, Inverted Pyramid Multi-task Transformer for Dense Scene Understanding. ECCV 2022
Cite
BibTex:
@InProceedings{invpt2022,
title={Inverted Pyramid Multi-task Transformer for Dense Scene Understanding},
author={Ye, Hanrong and Xu, Dan},
booktitle={ECCV},
year={2022}
}
@InProceedings{taskprompter2023,
title={TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding},
author={Ye, Hanrong and Xu, Dan},
booktitle={ICLR},
year={2023}
}
@article{ye2023invpt++,
title={InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding},
author={Ye, Hanrong and Xu, Dan},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024}
}
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Contact
Please contact Hanrong Ye if any questions.
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