Awesome
[OVSeg] Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
<img src="resources/pytorch-logo-dark.png" width="10%">This is the official PyTorch implementation of our paper: <br> Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP<br> Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, Diana Marculescu <br> Computer Vision and Pattern Recognition Conference (CVPR), 2023
[arXiv] [Project] [huggingface demo]
<p align="center"> <img src="resources/ovseg.gif" width="100%"> </p>Installation
Please see installation guide.
Data Preparation
Please see datasets preparation.
Getting started
Please see getting started instruction.
Finetuning CLIP
Please see open clip training.
LICENSE
The majority of OVSeg is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
However portions of the project are under separate license terms: CLIP and ZSSEG are licensed under the MIT license; MaskFormer is licensed under the CC-BY-NC; openclip is licensed under the license at its repo.
Citing OVSeg :pray:
If you use OVSeg in your research or wish to refer to the baseline results published in the paper, please use the following BibTeX entry.
@inproceedings{liang2023open,
title={Open-vocabulary semantic segmentation with mask-adapted clip},
author={Liang, Feng and Wu, Bichen and Dai, Xiaoliang and Li, Kunpeng and Zhao, Yinan and Zhang, Hang and Zhang, Peizhao and Vajda, Peter and Marculescu, Diana},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={7061--7070},
year={2023}
}