Zero-Shot Object Detection
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- license: apache-2.0
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+ ---
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+ license: apache-2.0
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+ ---
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+ [//]: # (<br />)
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+ <p align="center"> <h1 align="center">OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling</h1>
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+ <p align="center">
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+ <b> NeurIPS 2024 </b>
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+ <br />
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+ <a href="https://scholar.google.com.hk/citations?user=4rTE4ogAAAAJ&hl=zh-CN&oi=sra"><strong> Linhui Xiao </strong></a>
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+ <a href="https://yangxs.ac.cn/home"><strong>Xiaoshan Yang </strong></a>
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+ <a href="https://scholar.google.com.hk/citations?user=HBZ9plsAAAAJ&hl=zh-CN"><strong>Fang Peng </strong></a>
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+ <a href="https://scholar.google.com.hk/citations?user=o_DllmIAAAAJ&hl=zh-CN"><strong>Yaowei Wang </strong></a>
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+ <a href="https://scholar.google.com.hk/citations?user=hI9NRDkAAAAJ&hl=zh-CN"><strong>Changsheng Xu</strong></a>
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+ </p>
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+
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+ <p align="center">
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+ <a href='https://openreview.net/pdf?id=siPdcro6uD'>
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+ <img src='https://img.shields.io/badge/NeurIPS PDF-purple' alt='arXiv PDF'>
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+ </a>
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+ <a href='https://neurips.cc/virtual/2024/poster/93378'>
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+ <img src='https://img.shields.io/badge/NeurIPS Paper Homepage-blue' alt='arXiv PDF'>
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+ </a>
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+ <a href='https://neurips.cc/media/PosterPDFs/NeurIPS%202024/93378.png?t=1729402553.3015864'>
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+ <img src='https://img.shields.io/badge/NeurIPS Poster-lightblue' alt='arXiv PDF'>
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+ </a>
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+ <a href='https://neurips.cc/media/neurips-2024/Slides/93378_ROahXfO.pdf'>
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+ <img src='https://img.shields.io/badge/NeurIPS Slides-lightgreen' alt='arXiv PDF'>
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+ </a>
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+ <a href='https://arxiv.org/pdf/2410.08021'>
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+ <img src='https://img.shields.io/badge/arXiv-PDF-green?style=flat&logo=arXiv&logoColor=green' alt='arXiv PDF'>
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+ </a>
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+ <br />
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+ <p align="center"> <img src='docs/fig1.jpg' align="center" width="95%"> </p>
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+ **<p align="center"> A Comparison between OneRef model and the mainstream REC/RES architectures. </p>**
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+ This repository is the official Pytorch implementation for the paper [**OneRef: Unified One-tower Expression Grounding
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+ and Segmentation with Mask Referring Modeling**](https://openreview.net/pdf?id=siPdcro6uD), which is an advanced version
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+ of our preliminary work **HiVG** ([Publication](https://dl.acm.org/doi/abs/10.1145/3664647.3681071), [Paper](https://openreview.net/pdf?id=NMMyGy1kKZ),
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+ [Code](https://github.com/linhuixiao/HiVG)) and **CLIP-VG** ([Publication](https://ieeexplore.ieee.org/abstract/document/10269126),
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+ [Paper](https://arxiv.org/pdf/2305.08685), [Code](https://github.com/linhuixiao/CLIP-VG)).
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+ If you have any questions, please feel free to open an issue or contact me with emails: <xiaolinhui16@mails.ucas.ac.cn>.
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+ Any kind discussions are welcomed!