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spider-unicdseg's Introduction


Spider: A Unified Framework for Context-dependent Concept Segmentation

ICML, 2024
Xiaoqi Zhao* · Youwei Pang* . Wei Ji* . Baicheng Sheng . Jiaming Zuo · Lihe Zhang* · Huchuan Lu

arXiv PDF


Context-independent (CI) Concept vs. Context-dependent (CD) Concept


CD Concept Segmentation Survey Paper

(IJCV 2024) Towards Diverse Binary Segmentation via A Simple yet General Gated Network

Unified 8 CD Concept Segmentation Tasks


Spider: UniCDSeg Framework (You only train and infer once! 100% Unified Parameters.)


Performance





Potential

Continual/Zero-shot/Incremental Zero-shot learning



In-Context Learning


Datasets


Trained Models

Prediction Maps

To Do List

  • Release data sets.
  • Release model code.
  • Release model weights.
  • Release model prediction maps.

Citation

If you think Spider-UniCDSeg codebase are useful for your research, please consider referring us:

@inproceedings{Spider,
  title={Spider: A Unified Framework for Context-dependent Concept Segmentation},
  author={Zhao, Xiaoqi and Pang, Youwei and Ji, Wei and Sheng, Baicheng and Zuo, Jiaming and Zhang, Lihe and Lu, Huchuan},
  booktitle={ICML},
  year={2024}

spider-unicdseg's People

Contributors

xiaoqi-zhao-dlut avatar lartpang avatar

Stargazers

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Watchers

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spider-unicdseg's Issues

Code

Nice work, waiting for your code!

Question about environment

Thank you for sharing this work! I would ask if you will publish the requirements.txt of this project?

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