Awesome
<br /> <p align="center"> <h1 align="center">Spider: A Unified Framework for Context-dependent Concept Segmentation</h1> <p align="center"> ICML, 2024 <br /> <a href="https://xiaoqi-zhao-dlut.github.io/"><strong>Xiaoqi Zhao*</strong></a> · <a href="https://lartpang.github.io/"><strong>Youwei Pang*</strong></a> . <a href="https://jiwei0921.github.io/"><strong>Wei Ji*</strong></a> . <a ><strong>Baicheng Sheng</strong></a> . <a ><strong>Jiaming Zuo</strong></a> · <a href="https://scholar.google.com/citations?hl=zh-CN&user=XGPdQbIAAAAJ"><strong>Lihe Zhang*</strong></a> · <a href="https://scholar.google.com/citations?hl=zh-CN&user=D3nE0agAAAAJ"><strong>Huchuan Lu</strong></a> </p> <p align="center"> <a href='https://arxiv.org/pdf/2405.01002'> <img src='https://img.shields.io/badge/Paper-PDF-green?style=flat&logo=arXiv&logoColor=green' alt='arXiv PDF'> </a> </p> <br />Context-independent (CI) Concept vs. Context-dependent (CD) Concept
<p align="center"> <img src="./image/CI_vs_CD.png"/> <br /> </p>CD Concept Segmentation Survey Paper
(IJCV 2024) Towards Diverse Binary Segmentation via A Simple yet General Gated Network
Unified 8 CD Concept Segmentation Tasks
<p align="center"> <img src="./image/UniverCDSeg.png"/> <br /> </p>Spider: UniCDSeg Framework (You only train and infer once! 100% Unified Parameters.)
<p align="center"> <img src="./image/Spider.png"/> <br /> </p>Performance
<p align="center"> <img src="./image/performance1.png"/> <br /> </p> <p align="center"> <img src="./image/performance2.png"/> <br /> </p> <p align="center"> <img src="./image/performance3.png"/> <br /> </p> <p align="center"> <img src="./image/performance4.png"/> <br /> </p>Potential
Continual/Zero-shot/Incremental Zero-shot learning
<p align="center"> <img src="./image/ZSL1.png"/> <br /> </p> <p align="center"> <img src="./image/ZSL2.png"/> <br /> </p>In-Context Learning
<p align="center"> <img src="./image/In_context_learning.png"/> <br /> </p>Datasets
<p align="center"> <img src="./image/datasets.png"/> <br /> </p>- DUTS (SOD): Google Drive
- COD10K (COD): Google Drive
- SBU (SD): Google Drive
- Trans10K (TOS): Trans10K Website
- Five datasets (CPS): Google Drive
- COVID-19 data (COD): Google Drive
- BUSI (BLS): Google Drive
- ISIC18 (SLS): Google Drive
Trained Models
- Spider-ConvNext-B Google Drive
- Spider-ConvNext-L GitHub Release
- Spider-Swin-B Google Drive
- Spider-Swin-L GitHub Release
Prediction Maps
- Spider-Swin-B Google Drive
- Spider-Swin-L Google Drive
- Spider-ConvNeXt-B Google Drive
- Spider-ConvNeXt-L Google Drive
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}