GNM-PT

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temporary demo for RSAM-PT

GNM-PT 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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<h1 align="center"> Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition </h1>
<p align="center">
    <a href="https://arxiv.org/pdf/2410.21042"><img src="https://img.shields.io/badge/arXiv-2306.06963-b31b1b.svg" alt="Paper"></a>
    <a href="https://vcc.tech/research/2024/GNM-PT"><img alt="overview" src="https://img.shields.io/static/v1?label=overview&message=VCC%20Project&color=blue"></a>
    
    
    
    <a href="https://github.com/Keke921/GNM-PT/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/LFhase/PAIR?color=blue"></a>
    
    <a href="https://github.com/Keke921/GNM-PT/blob/main/Slides%20Poster%20Video/NeurIPS24_Slides.pptx"> <img src="https://img.shields.io/badge/Slides-grey?&logo=MicrosoftPowerPoint&logoColor=white" alt="Slides"></a>
    <a href="https://github.com/Keke921/GNM-PT/blob/main/Slides%20Poster%20Video/NeurIPS24_Slides.pdf"> <img src="https://img.shields.io/badge/Slides-grey?logo=airplayvideo&logoColor=white" alt="Slides"></a>
    <a href="https://github.com/Keke921/GNM-PT/blob/main/Slides%20Poster%20Video/NeurIPS24_Poster.pdf"> <img src="https://img.shields.io/badge/Poster-grey?logo=airplayvideo&logoColor=white" alt="Poster"></a>
    <a href="https://github.com/Keke921/GNM-PT/blob/main/Slides%20Poster%20Video/NeurIPS24_Video.mp4"> <img src="https://img.shields.io/badge/Video-grey?logo=airplayvideo&logoColor=white" alt="Video"></a>
</p>

# GNM-PT
Temporary demo for GNM-PT.

The core code is in [gnm.py](https://github.com/Keke921/RSAM-PT/blob/main/gnm.py).

## You May Find Our Additional Works of Interest

* [CVPR'22] Long-Tailed Visual Recognition via Gaussian Clouded Logit Adjustment [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Long-Tailed_Visual_Recognition_via_Gaussian_Clouded_Logit_Adjustment_CVPR_2022_paper.pdf)] [[code](https://github.com/Keke921/GCLLoss)]

* [TPAMI'23] Key Point Sensitive Loss for Long-Tailed Visual Recognition [[paper](https://drive.google.com/file/d/1gOJDHBJ_M7RmU6Iw2p6uXIyo8pNgVMrv/view?pli=1)] [[code](https://github.com/Keke921/KPSLoss)]

* [CVPR'23] Long-Tailed Visual Recognition via Self-heterogeneous Integration with Knowledge Excavation [[paper](https://arxiv.org/pdf/2304.01279)] [[code](https://github.com/jinyan-06/SHIKE)]

* [AAAI'24] Feature Fusion from Head to Tail for Long-Tailed Visual Recognition [[paper](https://arxiv.org/pdf/2306.06963)] [[code](https://github.com/Keke921/H2T)]

* [TAI'24] Adjusting logit in Gaussian form for long-tailed visual recognition [[paper](https://arxiv.org/pdf/2305.10648)] [[code](https://github.com/Keke921/GCLLoss)]

## Misc

If you find our paper and repo useful, please cite our paper:

```bibtex
@inproceedings{LiGNMPT,
 author = {Li, Mengke and Liu, Ye and Lu, Yang and Zhang, Yiqun and Cheung, Yiu-ming and Huang, Hui},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {103985--104009},
 title = {Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition},
 volume = {37},
 year = {2024}
}
```

## Acknowledgment
We refer to the code architecture from [VPT reproduce](https://github.com/DongSky/vpt_reproduce). Many thanks to the authors.

GNM-PT

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