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align=center\u003e𝓐𝔀𝓮𝓼𝓸𝓶𝓮 𝓟𝓪𝓻𝓪𝓶𝓮𝓽𝓮𝓻-𝓔𝓯𝓯𝓲𝓬𝓲𝓮𝓷𝓽 𝓣𝓻𝓪𝓷𝓼𝓯𝓮𝓻 𝓛𝓮𝓪𝓻𝓷𝓲𝓷𝓰\u003c/p\u003e","🐌 \u003cspan id=\"head1\"\u003e *Papers* \u003c/span\u003e","🎯 \u003cspan id=\"head1\"\u003e *Datasets of Visual PETL* \u003c/span\u003e","💬 \u003cspan id=\"head1\"\u003e *Keywords* \u003c/span\u003e"],"sub_categories":["Adapter Tuning","Prefix Tuning","Reparameter Tuning","Side Tuning","Prompt Tuning","Unified Tuning","Specification Tuning"],"readme":"## \u003cp align=center\u003e𝓐𝔀𝓮𝓼𝓸𝓶𝓮 𝓟𝓪𝓻𝓪𝓶𝓮𝓽𝓮𝓻-𝓔𝓯𝓯𝓲𝓬𝓲𝓮𝓷𝓽 𝓣𝓻𝓪𝓷𝓼𝓯𝓮𝓻 𝓛𝓮𝓪𝓻𝓷𝓲𝓷𝓰\u003c/p\u003e\n\u003cdiv align=center\u003e\n\n\u003cp\u003e\n\n ![GitHub stars](https://img.shields.io/github/stars/synbol/Awesome-Parameter-Efficient-Transfer-Learning.svg?color=red\u0026style=for-the-badge) \n ![GitHub forks](https://img.shields.io/github/forks/synbol/Awesome-Parameter-Efficient-Transfer-Learning.svg?style=for-the-badge) \n ![GitHub activity](https://img.shields.io/github/last-commit/synbol/Awesome-Parameter-Efficient-Transfer-Learning?color=yellow\u0026style=for-the-badge) \n ![GitHub issues](https://img.shields.io/github/issues/synbol/Awesome-Parameter-Efficient-Transfer-Learning?style=for-the-badge)\n\n [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)\n\u003c/p\u003e\n\n𝓐 𝓬𝓸𝓵𝓵𝓮𝓬𝓽𝓲𝓸𝓷 𝓸𝓯 𝓻𝓮𝓼𝓸𝓾𝓻𝓬𝓮𝓼 𝓸𝓷 𝓹𝓪𝓻𝓪𝓶𝓮𝓽𝓮𝓻-𝓮𝓯𝓯𝓲𝓬𝓲𝓮𝓷𝓽 𝓽𝓻𝓪𝓷𝓼𝓯𝓮𝓻 𝓵𝓮𝓪𝓻𝓷𝓲𝓷𝓰.\n\n\u003c/div\u003e\n\n## 📚 \u003cspan id=\"head1\"\u003e *Table of Contents* \u003c/span\u003e\n- [Introduction](#introduction)\n\n- [Keywords](#keywords)\n\n- [Papers](#papers)\n\n  - [Addition-based Tuning](#addition-based-tuning)\n    - [Adapter Tuning](#adapter-tuning)\u0026emsp;\u0026emsp;\u0026nbsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-30-2EA9DF?style=flat-square\u0026logo=data:image/svg%2bxml;base64,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\" alt=\"PaperNum\"/\u003e\n  \n    - [Prompt Tuning](#prompt-tuning)\u0026emsp;\u0026emsp;\u0026nbsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-27-90B44B?style=flat-square\u0026logo=data:image/svg%2bxml;base64,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\" alt=\"PaperNum\"/\u003e\n  \n    - [Prefix Tuning](#prefix-tuning)\u0026emsp;\u0026emsp;\u0026nbsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-5-B481BB?style=flat-square\u0026logo=data:image/svg%2bxml;base64,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\" alt=\"PaperNum\"/\u003e\n  \n    - [Side Tuning](#side-tuning)\u0026emsp;\u0026emsp;\u0026nbsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-15-F9BF45?style=flat-square\u0026logo=data:image/svg%2bxml;base64,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\" alt=\"PaperNum\"/\u003e\n  \n  - [Partial-based Tuning](#partial-based-tuning)\n    - [Specification Tuning](#specification-tuning)\u0026emsp;\u0026emsp;\u0026emsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-8-E83015?style=flat-square\u0026logo=data:image/svg%2bxml;base64,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\" alt=\"PaperNum\"/\u003e\n  \n    - [Reparameter Tuning](#reparameter-tuning)\u0026ensp;\u0026emsp;\u0026nbsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-10-2EA9DF?style=flat-square\u0026logo=data:image/svg%2bxml;base64,PHN2ZyBpZD0iQ2FwYV8xIiBlbmFibGUtYmFja2dyb3VuZD0ibmV3IDAgMCA1MTIgNTEyIiBoZWlnaHQ9IjUxMiIgdmlld0JveD0iMCAwIDUxMiA1MTIiIHdpZHRoPSI1MTIiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PGc+PHBhdGggZD0ibTM5NS44MiAxODIuNjE2LTE4OC43MiAxODguNzItMTIuOTEgMS43Mi05LjM1IDIwLjU0LTM0LjMxIDM0LjMxLTExLjAxLS43My0xMS4yNSAyMi45OS01Ni40OCA1Ni40OGMtMi45MyAyLjkzLTYuNzcgNC4zOS0xMC42MSA0LjM5cy03LjY4LTEuNDYtMTAuNjEtNC4zOWwtMjIuNjItMjIuNjJoLS4wMWwtMjIuNjItMjIuNjNjLTUuODYtNS44Ni01Ljg2LTE1LjM2IDAtMjEuMjJsNzcuNjMtNzcuNjMgMTYuNi03LjAzIDUuNjYtMTUuMjMgMzQuMzEtMzQuMzEgMTQuODQtNC45MiA3LjQyLTE3LjM0IDE2Ny41Ny0xNjcuNTcgMzMuMjQgMzMuMjR6IiBmaWxsPSIjZjY2Ii8+PHBhdGggZD0ibTM5NS44MiAxMTYuMTQ2djY2LjQ3bC0xODguNzIgMTg4LjcyLTEyLjkxIDEuNzItOS4zNSAyMC41NC0zNC4zMSAzNC4zMS0xMS4wMS0uNzMtMTEuMjUgMjIuOTktNTYuNDggNTYuNDhjLTIuOTMgMi45My02Ljc3IDQuMzktMTAuNjEgNC4zOXMtNy42OC0xLjQ2LTEwLjYxLTQuMzlsLTIyLjYyLTIyLjYyIDMzNC42NC0zMzQuNjR6IiBmaWxsPSIjZTYyZTZiIi8+PHBhdGggZD0ibTUwNi42MSAyMDkuMDA2LTY5LjE0LTY5LjEzIDQzLjA1LTg4LjM4YzIuOC01Ljc1IDEuNjUtMTIuNjUtMi44OC0xNy4xNy00LjUyLTQuNTMtMTEuNDItNS42OC0xNy4xNy0yLjg4bC04OC4zOCA0My4wNS02OS4xMy02OS4xNGMtNC4zNS00LjM1LTEwLjkyLTUuNi0xNi41Ni0zLjE2LTUuNjUgMi40NS05LjIzIDguMDktOS4wNCAxNC4yNGwyLjg2IDkwLjQ1LTg1LjM3IDU3LjgzYy00LjkxIDMuMzItNy40IDkuMjItNi4zNiAxNS4wNCAxLjA0IDUuODMgNS40IDEwLjUxIDExLjE1IDExLjk0bDk2LjYyIDI0LjAxIDI0LjAxIDk2LjYyYzEuNDMgNS43NSA2LjExIDEwLjExIDExLjk0IDExLjE1Ljg3LjE2IDEuNzUuMjMgMi42Mi4yMyA0LjkyIDAgOS42LTIuNDIgMTIuNDItNi41OWw1Ny44My04NS4zNyA5MC40NSAyLjg2YzYuMTQuMTkgMTEuNzktMy4zOSAxNC4yNC05LjA0IDIuNDQtNS42NCAxLjE5LTEyLjIxLTMuMTYtMTYuNTZ6IiBmaWxsPSIjZmFiZTJjIi8+PHBhdGggZD0ibTI5Ni4yNiAyMTUuNzA2IDI0LjAxIDk2LjYyYzEuNDMgNS43NSA2LjExIDEwLjExIDExLjk0IDExLjE1Ljg3LjE2IDEuNzUuMjMgMi42Mi4yMyA0LjkyIDAgOS42LTIuNDIgMTIuNDItNi41OWw1Ny44My04NS4zNyA5MC40NSAyLjg2YzYuMTQuMTkgMTEuNzktMy4zOSAxNC4yNC05LjA0IDIuNDQtNS42NCAxLjE5LTEyLjIxLTMuMTYtMTYuNTZsLTY5LjE0LTY5LjEzIDQzLjA1LTg4LjM4YzIuOC01Ljc1IDEuNjUtMTIuNjUtMi44OC0xNy4xN3oiIGZpbGw9IiNmZDkwMjUiLz48cGF0aCBkPSJtNDY1IDQxNi45NjZjLTI1LjkyIDAtNDcgMjEuMDgtNDcgNDdzMjEuMDggNDcgNDcgNDcgNDctMjEuMDggNDctNDctMjEuMDgtNDctNDctNDd6IiBmaWxsPSIjZmFiZTJjIi8+PHBhdGggZD0ibTEwNCAyOC45NjZoLTEzdi0xM2MwLTguMjg0LTYuNzE2LTE1LTE1LTE1cy0xNSA2LjcxNi0xNSAxNXYxM2gtMTNjLTguMjg0IDAtMTUgNi43MTYtMTUgMTVzNi43MTYgMTUgMTUgMTVoMTN2MTNjMCA4LjI4NCA2LjcxNiAxNSAxNSAxNXMxNS02LjcxNiAxNS0xNXYtMTNoMTNjOC4yODQgMCAxNS02LjcxNiAxNS0xNXMtNi43MTYtMTUtMTUtMTV6IiBmaWxsPSIjZmVkODQzIi8+PHBhdGggZD0ibTIwNy4xIDM3MS4zMzYtMjIuMjYgMjIuMjYtNDUuMzItODcuNjIgMjIuMjYtMjIuMjZ6IiBmaWxsPSIjZmVkODQzIi8+PHBhdGggZD0ibTE4NC44NCAzOTMuNTk2IDIyLjI2LTIyLjI2LTIyLjY2LTQzLjgxLTIyLjI2NSAyMi4yNjV6IiBmaWxsPSIjZmFiZTJjIi8+PHBhdGggZD0ibTE1MC41MyA0MjcuOTA2LTIyLjI2IDIyLjI2LTQ1LjMyLTg3LjYyIDIyLjI2LTIyLjI2eiIgZmlsbD0iI2ZlZDg0MyIvPjxwYXRoIGQ9Im0xMjguMjcgNDUwLjE2NiAyMi4yNi0yMi4yNi0yMi42NTUtNDMuODE1LTIyLjI2IDIyLjI2eiIgZmlsbD0iI2ZhYmUyYyIvPjxjaXJjbGUgY3g9IjE1IiBjeT0iMTE5Ljk2OSIgZmlsbD0iIzVlZDhkMyIgcj0iMTUiLz48Y2lyY2xlIGN4PSIxMjgiIGN5PSIxOTkuOTY5IiBmaWxsPSIjZDU5OWVkIiByPSIxNSIvPjxjaXJjbGUgY3g9IjE5MiIgY3k9IjYzLjk2NCIgZmlsbD0iI2Y2NiIgcj0iMTUiLz48Y2lyY2xlIGN4PSIzMjgiIGN5PSI0MTUuOTY3IiBmaWxsPSIjMzFiZWJlIiByPSIxNSIvPjxjaXJjbGUgY3g9IjQ0MCIgY3k9IjMyNy45NjciIGZpbGw9IiNhZDc3ZTMiIHI9IjE0Ljk5OSIvPjwvZz48L3N2Zz4=\" alt=\"PaperNum\"/\u003e\n  \n  - [Unified Tuning](#unified-tuning)\u0026ensp;\u0026emsp;\u0026nbsp;\u003cimg src=\"https://img.shields.io/badge/Number%20of%20Papers-6-B481BB?style=flat-square\u0026logo=data:image/svg%2bxml;base64,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\" alt=\"PaperNum\"/\u003e\n  \n- [Datasets](#datasets-of-visual-peft)\n\n- [Contribution](#contribution)\n\n## 📝 \u003cspan id=\"head1\"\u003e *Introduction* \u003c/span\u003e\n* **Parameter-Efficient Fine-Tuning (PEFT)** seeks to exceed the performance of full fine-tuning with minimal parameter modifications.\n* This repository provides a comprehensive overview and offer a systematic review of the latest advancements. It introduces a categorization criterion that classifies existing methods into three categories: **Addition-based Tuning, Partial-based Tuning, and Unified-based Tuning**.\n* This repository also introduces commonly used datasets and applications.\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"Overview.png\" width=\"100%\" height=\"100%\"\u003e\n\u003c/div\u003e\n\n## 💬 \u003cspan id=\"head1\"\u003e *Keywords* \u003c/span\u003e\n![](https://img.shields.io/badge/Abbreviation-blue) The abbreviation of the work.\n\n![](https://img.shields.io/badge/Application-green) The main explored task/application of the work.\n\n![](https://img.shields.io/badge/Other-orange) Other important information of the work.\n\n## 🐌 \u003cspan id=\"head1\"\u003e *Papers* \u003c/span\u003e\n### Addition-based Tuning\n### Adapter Tuning\n- **[1] AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition,** NeurIPS 2022.\n  \n  *Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, Ping Luo.*\n\n  [[Paper](https://arxiv.org/abs/2205.13535)][[Code](https://github.com/ShoufaChen/AdaptFormer)] ![](https://img.shields.io/badge/AdaptFormer-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Video_Recognition-green)  ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[2] Convolutional Bypasses are Better Vision Transformer Adapters,** Arxiv 2022.\n  \n  *Jie, Shibo and Deng, Zhi-Hong.*\n\n  [[Paper](https://arxiv.org/abs/2207.07039)][[Code](https://github.com/JieShibo/PETL-ViT)] ![](https://img.shields.io/badge/Convpass-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Domain_Generalization-green)  ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[3] ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning,** NeurIPS 2022.\n  \n  *Pan, Junting and Lin, Ziyi and Zhu, Xiatian and Shao, Jing and Li, Hongsheng.*\n\n  [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/a92e9165b22d4456fc6d87236e04c266-Abstract-Conference.html)][[Code](https://github.com/linziyi96/ST-Adapter)] ![](https://img.shields.io/badge/ST_Adapter-blue) ![](https://img.shields.io/badge/Video_Recognition-green)  ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[4] AIM: Adapting Image Models for Efficient Video Action Recognition,** ICLR 2023.\n  \n  *Yang, Taojiannan and Zhu, Yi and Xie, Yusheng and Zhang, Aston and Chen, Chen and Li, Mu.*\n\n  [[Paper](https://arxiv.org/abs/2302.03024)][[Code](https://adapt-image-models.github.io/)] ![](https://img.shields.io/badge/AIM-blue) ![](https://img.shields.io/badge/Video_Recognition-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[5] Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation,** ICLR 2023.\n  \n  *Sharma, Mohit and Fantacci, Claudio and Zhou, Yuxiang and Koppula, Skanda and others.*\n\n  [[Paper](https://arxiv.org/abs/2304.06600)][[Code](https://sites.google.com/view/robo-adapters/)] ![](https://img.shields.io/badge/Robo_Adapter-blue) ![](https://img.shields.io/badge/Robotic_Manipulation-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[6] 1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions,** CVPR 2023.\n  \n  *Yin, Dongshuo and Yang, Yiran and Wang, Zhechao and Yu, Hongfeng and Wei, Kaiwen and Sun, Xian.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.html)][Code] ![](https://img.shields.io/badge/LoRand-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[7] Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks,** NeurIPS 2022.\n  \n  *Yen-Cheng Liu, Chih-Yao Ma, Junjiao Tian, Zijian He, Zsolt Kira.*\n\n  [[Paper](https://arxiv.org/abs/2210.03265)][Code] ![](https://img.shields.io/badge/Polyhistor-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[8] VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding,** AAAI 2024.\n\n  *Yi Xin, Junlong Du, Qiang Wang, Zhiwen Lin, Ke Yan.*\n\n  [[Paper](https://arxiv.org/abs/2312.08733)][[Code]()] ![](https://img.shields.io/badge/VMT_Adapter-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[9] SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels,** IJCV 2023.\n\n  *Henry Hengyuan Zhao, Pichao Wang, Yuyang Zhao, Hao Luo, Fan Wang, Mike Zheng Shou.*\n\n  [[Paper](https://arxiv.org/abs/2303.07910)][[Code](https://github.com/showlab/SCT)] ![](https://img.shields.io/badge/SCT-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[10] Important Channel Tuning,** Openreview 2023.\n\n  *Hengyuan Zhao, Pichao WANG, Yuyang Zhao, Fan Wang, Mike Zheng Shou.*\n\n  [[Paper](https://openreview.net/forum?id=TTMyoOdB9hZ)][Code] ![](https://img.shields.io/badge/ICT-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[11] Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm,** Arxiv 2023.\n\n  *Zhao, Hengyuan and Luo, Hao and Zhao, Yuyang and Wang, Pichao and Wang, Fan and Shou, Mike Zheng.*\n\n  [[Paper](https://arxiv.org/abs/2303.07910)][Code] ![](https://img.shields.io/badge/TTC_Tuning-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[12] Compacter: Efficient Low-Rank Hypercomplex Adapter Layer,** NeurIPS 2021.\n  \n  *Karimi Mahabadi, Rabeeh and Henderson, James and Ruder, Sebastian.*\n\n  [[Paper](https://arxiv.org/abs/2106.04647)][[Code](https://github.com/rabeehk/compacter)] ![](https://img.shields.io/badge/COMPACTER-blue) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[13] Parameter-efficient and student-friendly knowledge distillation,** NeurIPS 2022.\n  \n  *Rao, Jun and Meng, Xv and Ding, Liang and Qi, Shuhan and Tao, Dacheng.*\n\n  [[Paper](https://arxiv.org/abs/2205.15308)][Code] ![](https://img.shields.io/badge/PESF_KD-blue)\n\n- **[14] VL-adapter: Parameter-efficient transfer learning for vision-and-language tasks,** CVPR 2022.\n  \n  *Sung, Yi-Lin and Cho, Jaemin and Bansal, Mohit.*\n\n  [[Paper](https://arxiv.org/abs/2112.06825)][[Code](https://github.com/ylsung/VL_adapter)] ![](https://img.shields.io/badge/VL_adapter-blue) ![](https://img.shields.io/badge/Cross_Modal-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n  \n- **[15] UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling,** ICLR 2024.\n  \n  *Haoyu Lu, Mingyu Ding, Yuqi Huo, Guoxing Yang, Zhiwu Lu, Masayoshi Tomizuka, Wei Zhan.*\n\n  [[Paper](https://arxiv.org/abs/2302.06605)][[Code](https://github.com/RERV/UniAdapter)] ![](https://img.shields.io/badge/UniAdapter-blue) ![](https://img.shields.io/badge/Cross_Modal-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[16] Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model,** Arxiv 2023.\n\n  *Zelin Peng, Zhengqin Xu, Zhilin Zeng, Lingxi Xie, Qi Tian, and Wei Shen.*\n  \n  [[Paper](https://arxiv.org/pdf/2311.17112.pdf)][Code] ![](https://img.shields.io/badge/SAM_COBOT-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[17] Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning,** Arxiv 2023.\n\n  *Sanghyeon Kim, Hyunmo Yang, Younghyun Kim, Youngjoon Hong, Eunbyung Park.*\n  \n  [[Paper](https://arxiv.org/abs/2309.06922)][[Code](https://github.com/extremebird/Hydra/tree/main)] ![](https://img.shields.io/badge/Hydra-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[18] MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering,** CVPR 2023.\n\n  *Jingjing Jiang, Nanning Zheng.*\n  \n  [[Paper](https://web3.arxiv.org/abs/2303.01239)][[Code](https://github.com/jingjing12110/MixPHM)] ![](https://img.shields.io/badge/MixPHM-blue) ![](https://img.shields.io/badge/Cross_Modal-green) ![](https://img.shields.io/badge/Adapter_Optimization-orange)\n\n- **[19] Vision Transformers are Parameter-Efficient Audio-Visual Learners,** CVPR 2023.\n\n  *Yan-Bo Lin, Yi-Lin Sung, Jie Lei, Mohit Bansal, Gedas Bertasius.*\n  \n  [[Paper](https://arxiv.org/abs/2212.07983)][[Code](https://genjib.github.io/project_page/LAVISH/)] ![](https://img.shields.io/badge/LAVISH-blue) ![](https://img.shields.io/badge/Cross_Modal-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[20] SAM-Adapter: Adapting Segment Anything in Underperformed Scenes,** ICCVW 2023.\n\n  *Chen, Tianrun and Zhu, Lanyun and Deng, Chaotao and Cao, Runlong and Wang, Yan and Zhang, Shangzhan and Li, Zejian and Sun, Lingyun and Zang, Ying and Mao, Papa.*\n  \n  [[Paper](https://openaccess.thecvf.com/content/ICCV2023W/VCL/papers/Chen_SAM-Adapter_Adapting_Segment_Anything_in_Underperformed_Scenes_ICCVW_2023_paper.pdf)][[Code](http://research.kokoni3d.com/sam-adapter)] ![](https://img.shields.io/badge/SAM-Adapter-blue) ![](https://img.shields.io/badge/Image_Segmentation-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n  \n- **[21] T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models,** AAAI 2024.\n\n  *Mou, Chong and Wang, Xintao and Xie, Liangbin and Zhang, Jian and Qi, Zhongang and others.*\n  \n  [[Paper](https://arxiv.org/abs/2302.08453)][[Code](https://github.com/TencentARC/T2I-Adapter)] ![](https://img.shields.io/badge/T2I_Adapter-blue) ![](https://img.shields.io/badge/Text2Image-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[22] I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models,** Arxiv 2023.\n\n  *Guo, Xun and Zheng, Mingwu and Hou, Liang and Gao, Yuan and Deng, Yufan and others.*\n  \n  [[Paper](https://arxiv.org/abs/2312.16693)][[Code](https://github.com/I2V-Adapter/I2V-Adapter-repo)] ![](https://img.shields.io/badge/I2V_Adapter-blue) ![](https://img.shields.io/badge/Image2Video-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[23] AdaptIR: Parameter Efficient Multi-task Adaptation for Pre-trained Image Restoration Models,** Arxiv 2023.\n\n  *Hang Guo, Tao Dai, Yuanchao Bai, Bin Chen, Shu-Tao Xia, Zexuan Zhu.*\n\n  [[Paper](https://arxiv.org/pdf/2312.08881.pdf)][[Code](https://github.com/csguoh/AdaptIR)] ![](https://img.shields.io/badge/AdaptIR-blue) ![](https://img.shields.io/badge/Super_Resolution-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[24] A Closer Look at Parameter-Efficient Tuning in Diffusion Models,** Arxiv 2023.\n\n  *Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu.*\n  \n  [[Paper](https://arxiv.org/abs/2303.18181)][[Code](https://github.com/Xiang-cd/unet-finetune)] ![](https://img.shields.io/badge/Unet_Finetune-blue) ![](https://img.shields.io/badge/Generate_Task-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[25] CAST: Cross-Attention in Space and Time for Video Action Recognition,** NeurIPS 2023.\n\n  *Lee, Dongho and Lee, Jongseo and Choi, Jinwoo.*\n  \n  [[Paper](https://proceedings.neurips.cc/paper_files/paper/2023/file/fb1b83b35e96998ddfc0ce1dab635445-Paper-Conference.pdf)][[Code](https://github.com/KHU-VLL/CAST)] ![](https://img.shields.io/badge/CAST_Finetune-blue) ![](https://img.shields.io/badge/Video_Action_Recognition-green) ![](https://img.shields.io/badge/Adapter_Design-orange)\n\n- **[26] Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis,** CVPR 2024.\n\n  *Xin Zhou , Dingkang Liang , Wei Xu, Xingkui Zhu ,Yihan Xu, Zhikang Zou, Xiang Bai.*\n  \n  [[Paper](https://arxiv.org/abs/2403.01439)][[Code](https://github.com/LMD0311/DAPT)] ![](https://img.shields.io/badge/DAPT-blue) ![](https://img.shields.io/badge/Point_Cloud-green) ![](https://img.shields.io/badge/Adapter_with_Prompt-orange)\n\n- **[27] MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation,** ArXiv 2024.\n\n  *Kunpeng Song and Yizhe Zhu and Bingchen Liu and Qing Yan and Ahmed Elgammal and Xiao Yang.*\n  \n  [[Paper](https://arxiv.org/pdf/2404.05674.pdf)][[Code](https://moma-adapter.github.io/)] ![](https://img.shields.io/badge/MoMA-blue) ![](https://img.shields.io/badge/Personalized_Image_Generation-green) ![](https://img.shields.io/badge/Adapter_with_Prompt-orange)\n\n- **[28] Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation,** ICCV 2023.\n\n  *Zunnan Xu, Zhihong Chen, Yong Zhang, Yibing Song, Xiang Wan, Guanbin Li.*\n\n  [[Paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Xu_Bridging_Vision_and_Language_Encoders_Parameter-Efficient_Tuning_for_Referring_Image_ICCV_2023_paper.pdf)][[Code](https://github.com/kkakkkka/ETRIS)]\n\n- **[29] Enhancing Fine-grained Multi-modal Alignment via Adapters: A Parameter-Efficient Training Framework for Referring Image Segmentation,** WANT @ ICML 2024.\n\n  *Zunnan Xu, Jiaqi Huang, Ting Liu, Yong Liu, Haonan Han, Kehong Yuan, Xiu Li.*\n\n  [[Paper](https://openreview.net/forum?id=bp8xXLi2Mp)][[Code](https://kkakkkka.github.io/dcris)] \n\n- **[30] Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference,** ArXiv 2024.\n\n  *Ting Liu, Xuyang Liu, Liangtao Shi, Zunnan Xu, Siteng Huang, Yi Xin, Quanjun Yin.*\n\n  [[Paper](https://arxiv.org/pdf/2405.14700)][[Code](https://github.com/liuting20/Sparse-Tuning)]\n\n- **[30] PAVE: Patching and Adapting Video Large Language Models,** CVPR 2025.\n\n  *Zhuoming Liu, Yiquan Li, Khoi Duc Nguyen, Yiwu Zhong, Yin Li.*\n\n  [[Paper](https://arxiv.org/abs/2503.19794)][[Code](https://github.com/dragonlzm/PAVE)]\n\n\n\n### Prompt Tuning\n- **[1] Visual Prompt Tuning,** ECCV 2022.\n  \n  *Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, Ser-Nam Lim.*\n\n  [[Paper](https://arxiv.org/abs/2203.12119)][[Code](https://github.com/kmnp/vpt)] ![](https://img.shields.io/badge/VPT-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[2] Visual Prompt Tuning for Test-time Domain Adaptation,** Arxiv 2022.\n  \n  *Gao, Yunhe and Shi, Xingjian and Zhu, Yi and Wang, Hao and Tang, Zhiqiang and Zhou, Xiong and others.*\n\n  [[Paper](https://arxiv.org/pdf/2210.04831.pdf)][Code] ![](https://img.shields.io/badge/DePT-blue) ![](https://img.shields.io/badge/Test_Time_Adaptation-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[3] LPT: Long-tailed Prompt Tuning for Image Classification,** ICLR 2023.\n  \n  *Dong, Bowen and Zhou, Pan and Yan, Shuicheng and Zuo, Wangmeng.*\n\n  [[Paper](https://arxiv.org/abs/2210.01033)][[Code](https://github.com/DongSky/LPT)] ![](https://img.shields.io/badge/LPT-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[4] Pro-tuning: Unified Prompt Tuning for Vision Tasks,** TCSVT 2023.\n  \n  *Nie, Xing and Ni, Bolin and Chang, Jianlong and Meng, Gaofeng and Huo, Chunlei and others.*\n\n  [[Paper](https://arxiv.org/abs/2207.14381)][Code] ![](https://img.shields.io/badge/Pro_tuning-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[5] Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models,** ICCV 2023.\n  \n  *Zha, Yaohua and Wang, Jinpeng and Dai, Tao and Chen, Bin and Wang, Zhi and Xia, Shu-Tao.*\n\n  [[Paper](https://arxiv.org/pdf/2304.07221.pdf)][[Code](https://github.com/zyh16143998882/ICCV23-IDPT)] ![](https://img.shields.io/badge/IDPT-blue) ![](https://img.shields.io/badge/Point_Cloud-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n\n- **[6] Visual Prompt Multi-Modal Tracking,** CVPR 2023.\n  \n  *Zhu, Jiawen and Lai, Simiao and Chen, Xin and Wang, Dong and Lu, Huchuan.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Visual_Prompt_Multi-Modal_Tracking_CVPR_2023_paper.html)][[Code](https://github.com/jiawen-zhu/ViPT)] ![](https://img.shields.io/badge/ViPT-blue) ![](https://img.shields.io/badge/MultiModal_Tracking-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[7] LION: Implicit Vision Prompt Tuning,** AAAI 2024.\n  \n  *Wang, Haixin and Chang, Jianlong and Luo, Xiao and Sun, Jinan and Lin, Zhouchen and Tian, Qi.*\n\n  [[Paper](https://arxiv.org/abs/2303.09992)][Code] ![](https://img.shields.io/badge/LION-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n\n- **[8] Convolutional Visual Prompt for Robust Visual Perception,** NeurIPS 2023.\n  \n  *Tsai, Yun-Yun and Mao, Chengzhi and Yang, Junfeng.*\n\n  [[Paper](https://openreview.net/forum?id=qgmrC8jhCo)][Code] ![](https://img.shields.io/badge/CVP-blue) ![](https://img.shields.io/badge/Test_Time_Adaptation-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n\n\n- **[9] ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation,** Arxiv 2023.\n  \n  *Hu, Shishuai and Liao, Zehui and Xia, Yong.*\n\n  [[Paper](https://arxiv.org/abs/2211.11514)][[Code](https://github.com/ShishuaiHu/ProSFDA)] ![](https://img.shields.io/badge/ProSFDA-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n\n- **[10] Explicit Visual Prompting for Low-Level Structure Segmentations,** CVPR 2023.\n  \n  *Liu, Weihuang and Shen, Xi and Pun, Chi-Man and Cun, Xiaodong.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Explicit_Visual_Prompting_for_Low-Level_Structure_Segmentations_CVPR_2023_paper.html)][[Code](https://github.com/NiFangBaAGe/Explicit-Visual-Prompt)] ![](https://img.shields.io/badge/EVP-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Pixel_Level-orange)\n\n- **[11] P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting,** NeurIPS 2022.\n  \n  *Wang, Ziyi and Yu, Xumin and Rao, Yongming and Zhou, Jie and Lu, Jiwen.*\n\n  [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/5cd6dc946ccc37ae6c9f4fc6b6181e1d-Abstract-Conference.html)][[Code](https://github.com/wangzy22/P2P)] ![](https://img.shields.io/badge/P2P-blue) ![](https://img.shields.io/badge/Point_Cloud-green) ![](https://img.shields.io/badge/Pixel_Level-orange)\n\n- **[12] Exploring Visual Prompts for Adapting Large-Scale Models,** Arxiv 2022.\n  \n  *Bahng, Hyojin and Jahanian, Ali and Sankaranarayanan, Swami and Isola, Phillip.*\n\n  [[Paper](https://arxiv.org/abs/2203.17274)][[Code](https://hjbahng.github.io/visual_prompting/)] ![](https://img.shields.io/badge/VP-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Pixel_Level-orange)\n\n- **[13] Unleashing the Power of Visual Prompting At the Pixel Level,** Arxiv 2023.\n  \n  *Wu, Junyang and Li, Xianhang and Wei, Chen and Wang, Huiyu and Yuille, Alan and Zhou, Yuyin and Xie, Cihang.*\n\n  [[Paper](https://arxiv.org/abs/2212.10556)][[Code](https://github.com/UCSC-VLAA/EVP)] ![](https://img.shields.io/badge/EVP-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Pixel_Level-orange)\n\n- **[14] Understanding and Improving Visual Prompting: A Label-Mapping Perspective,** CVPR 2023.\n  \n  *Chen, Aochuan and Yao, Yuguang and Chen, Pin-Yu and Zhang, Yihua and Liu, Sijia.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Understanding_and_Improving_Visual_Prompting_A_Label-Mapping_Perspective_CVPR_2023_paper.html)][[Code](https://github.com/OPTML-Group/ILM-VP)] ![](https://img.shields.io/badge/ILM_VP-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Pixel_Level-orange)\n\n\n- **[15] Learning to Prompt for Vision-Language Models,** IJCV 2022.\n  \n  *Kaiyang Zhou, Jingkang Yang, Chen Change Loy, Ziwei Liu.*\n\n  [[Paper](https://arxiv.org/abs/2109.01134)][[Code](https://github.com/KaiyangZhou/CoOp)] ![](https://img.shields.io/badge/CoOp-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Text_Prompt-orange)\n\n- **[16] Hyperprompt: Prompt-based task-conditioning of transformers,** ICML 2022.\n  \n  *He, Yun and Zheng, Steven and Tay, Yi and Gupta, Jai and Du, Yu and Aribandi, Vamsi and others.*\n\n  [[Paper](https://proceedings.mlr.press/v162/he22f.html)][Code] ![](https://img.shields.io/badge/HyperPrompt-blue) ![](https://img.shields.io/badge/Multi_Task-green)\n\n- **[17] MaPLe: Multi-modal Prompt Learning,** CVPR 2023.\n  \n  *Khattak, Muhammad Uzair and Rasheed, Hanoona and Maaz, Muhammad and others.*\n\n  [[Paper](https://arxiv.org/abs/2210.03117)][[Code](https://github.com/muzairkhattak/multimodal-prompt-learning)] ![](https://img.shields.io/badge/MaPLe-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/MultiModal_Prompt-orange)\n\n- **[18] Hierarchical Prompt Learning for Multi-Task Learning,** CVPR 2023.\n  \n  *Liu, Yajing and Lu, Yuning and Liu, Hao and An, Yaozu and Xu, Zhuoran and Yao, Zhuokun and others.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Hierarchical_Prompt_Learning_for_Multi-Task_Learning_CVPR_2023_paper.html)][Code] ![](https://img.shields.io/badge/HiPro-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Text_Prompt-orange)\n\n- **[19] Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving,** CVPR 2023.\n  \n  *Liang, Xiwen and Niu, Minzhe and Han, Jianhua and Xu, Hang and Xu, Chunjing and Liang, Xiaodan.*\n\n  [[Paper](https://arxiv.org/abs/2303.01788)][Code] ![](https://img.shields.io/badge/VE_Prompt-blue) ![](https://img.shields.io/badge/Multi_Task-green) ![](https://img.shields.io/badge/Autonomous_Driving-green)\n\n- **[20] Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model,** TMM 2023.\n  \n  *Xing, Yinghui and Wu, Qirui and Cheng, De and Zhang, Shizhou and Liang, Guoqiang and others.*\n\n  [[Paper](https://ieeexplore.ieee.org/abstract/document/10171397/)][[Code](https://github.com/fanrena/DPT)] ![](https://img.shields.io/badge/DPT-blue) ![](https://img.shields.io/badge/Image_Recognition-green) \n\n- **[21] Tokenize Anything via Prompting,** Arxiv 2023.\n  \n  *Pan, Ting and Tang, Lulu and Wang, Xinlong and Shan, Shiguang.*\n\n  [[Paper](https://arxiv.org/pdf/2312.09128.pdf)][[Code](https://github.com/baaivision/tokenize-anything)] ![](https://img.shields.io/badge/DPT-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) \n\n- **[22] MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning,** AAAI 2024.\n  \n  *Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding.*\n\n  [[Paper](https://arxiv.org/abs/2312.08636)][Code] ![](https://img.shields.io/badge/MmAP-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/MultiModal_Prompt-orange)\n\n- **[23] Diversity-Aware Meta Visual Prompting,** CVPR 2023.\n  \n  *Qidong Huang, Xiaoyi Dong, Dongdong Chen, Weiming Zhang, Feifei Wang, Gang Hua, Nenghai Yu.*\n\n  [[Paper](https://arxiv.org/abs/2303.08138)][[Code](https://arxiv.org/abs/2303.08138)] ![](https://img.shields.io/badge/DAM-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Pixel_Level-orange)\n\n- **[24] Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks,** NeurIPS 2023.\n  \n  *Duan, Haoyi and Xia, Yan and Mingze, Zhou and Tang, Li and Zhu, Jieming and Zhao, Zhou.*\n\n  [[Paper](https://proceedings.neurips.cc/paper_files/paper/2023/file/af01716e08073368a7c8a62be46dba17-Paper-Conference.pdf)][[Code](https://github.com/haoyi-duan/DG-SCT)] ![](https://img.shields.io/badge/DG-SCT-blue) ![](https://img.shields.io/badge/Audio-visual_Understanding-green) ![](https://img.shields.io/badge/MultiModal_Prompt-orange)\n  \n- **[25] Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models,** AAAI 2024.\n\n  *Yiwen Tang, Ray Zhang, Zoey Guo, Xianzheng Ma, Dong Wang, Zhigang Wang, Bin Zhao, Xuelong Li.*\n\n  [[Paper](https://arxiv.org/abs/2310.03059)][Code] ![](https://img.shields.io/badge/Point_PEFT-blue) ![](https://img.shields.io/badge/Point_Cloud-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[26] E2VPT: An Effective and Efficient Approach for Visual Prompt Tuning,** ICCV 2023.\n  \n  *Cheng, Han and Qifan, Wang and Yiming, Cui and Zhiwen, Cao and Wenguan, Wang and Siyuan, Qi and Dongfang, Liu*\n  \n  [[Paper](https://arxiv.org/abs/2307.13770)][[Code](https://github.com/ChengHan111/E2VPT)] ![](https://img.shields.io/badge/E2VPT-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Embedding_Level-orange)\n\n- **[27] DGL: Dynamic Global-Local Prompt Tuning for Text-Video Retrieval,** AAAI 2024.\n  \n  *Xiangpeng Yang and Linchao Zhu and Xiaohan Wang and Yi Yang*\n  \n  [[paper]](https://arxiv.org/abs/2401.10588) [[code]](https://github.com/knightyxp/DGL) ![](https://img.shields.io/badge/DGL-blue) ![](https://img.shields.io/badge/Text_Video_Retrieval-green) ![](https://img.shields.io/badge/Global_Local_Prompt_-orange)\n\n\n\n\n### Prefix Tuning\n- **[1] Prefix-Tuning: Optimizing Continuous Prompts for Generation,** ACL 2021.\n  \n  *Li, Xiang Lisa and Liang, Percy.* \n\n  [[Paper](https://arxiv.org/abs/2101.00190)][[Code](https://github.com/XiangLi1999/PrefixTuning)] ![](https://img.shields.io/badge/Prefix_Tuning-blue)\n\n- **[2] Towards a Unified View on Visual Parameter-Efficient Transfer Learning,** Arxiv 2023.\n  \n  *Yu, Bruce XB and Chang, Jianlong and Liu, Lingbo and Tian, Qi and Chen, Chang Wen.*\n\n  [[Paper](https://arxiv.org/abs/2210.00788)][[Code](https://github.com/bruceyo/V-PETL)] ![](https://img.shields.io/badge/V_PETL-blue) ![](https://img.shields.io/badge/Video_Recognition-green) ![](https://img.shields.io/badge/Unified_View-orange)\n\n- **[3] Exploring Efficient Few-shot Adaptation for Vision Transformers,** TMLR 2023.\n  \n  *Xu, Chengming and Yang, Siqian and Wang, Yabiao and Wang, Zhanxiong and Fu, Yanwei and Xue, Xiangyang.*\n\n  [[Paper](https://arxiv.org/pdf/2301.02419.pdf)][[Code](https://github.com/chmxu/eTT_TMLR2022)] ![](https://img.shields.io/badge/eTT-blue) ![](https://img.shields.io/badge/Few_Shot_Learning-green)\n\n- **[4] Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning,** CVPR 2023.\n  \n  *Tu, Cheng-Hao and Mai, Zheda and Chao, Wei-Lun.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Visual_Query_Tuning_Towards_Effective_Usage_of_Intermediate_Representations_for_CVPR_2023_paper.html)][[Code](https://github.com/andytu28/VQT)] ![](https://img.shields.io/badge/VQT-blue) ![](https://img.shields.io/badge/Image_Recognition-green)\n\n- **[5] A Unified Continual Learning Framework with General Parameter-Efficient Tuning,** ICCV 2023.\n  \n  *Tu, Cheng-Hao and Mai, Zheda and Chao, Wei-Lun.*\n\n  [[Paper](https://arxiv.org/pdf/2303.10070.pdf)][[Code](https://github.com/gqk/LAE)] ![](https://img.shields.io/badge/LAM-blue) ![](https://img.shields.io/badge/Continua_lLearning-green) ![](https://img.shields.io/badge/Framework-orange)\n\n\n### Side Tuning\n- **[1] Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks,** ECCV 2020.\n  \n  *Zhang, Jeffrey O and Sax, Alexander and Zamir, Amir and Guibas, Leonidas and Malik, Jitendra.*\n\n  [[Paper](https://arxiv.org/pdf/1912.13503.pdf)][Code] ![](https://img.shields.io/badge/Side_Tuning-blue) ![](https://img.shields.io/badge/Param_Efficient-orange)\n\n- **[2] LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning,** NeurIPS 2022.\n\n  *Sung, Yi-Lin and Cho, Jaemin and Bansal, Mohit.*\n\n  [[Paper](https://arxiv.org/abs/2206.06522)][[Code](https://github.com/ylsung/Ladder-Side-Tuning)] ![](https://img.shields.io/badge/LST-blue) ![](https://img.shields.io/badge/Cross_Modal-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[3] Vision transformer adapter for dense predictions.** ICLR 2023.\n\n  *Chen, Zhe and Duan, Yuchen and Wang, Wenhai and He, Junjun and Lu, Tong and Dai, Jifeng and Qiao, Yu.*\n\n  [[Paper](https://arxiv.org/abs/2205.08534)][[Code](https://github.com/czczup/ViT-Adapter)] ![](https://img.shields.io/badge/ViT_Adapter-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Param_Efficient-orange)\n\n- **[4] Side Adapter Network for Open-Vocabulary Semantic Segmentation,** CVPR 2023.\n  \n  *Xu, Mengde and Zhang, Zheng and Wei, Fangyun and Hu, Han and Bai, Xiang.*\n\n  [[Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Side_Adapter_Network_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2023_paper.html)][[Code](https://mendelxu.github.io/SAN/)] ![](https://img.shields.io/badge/SAN-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Param_Efficient-orange)\n\n- **[5] Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone,** NeurIPS 2023.\n  \n  *Jiang, Zeyinzi and Mao, Chaojie and Huang, Ziyuan and Ma, Ao and Lv, Yiliang and Shen, Yujun and Zhao, Deli and Zhou Jingren.*\n\n  [[Paper](https://arxiv.org/pdf/2310.19859.pdf)] [Code] ![](https://img.shields.io/badge/Res_Tuning-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[6] DTL: Disentangled Transfer Learning for Visual Recognition,** AAAI 2024.\n  \n  *Fu, Minghao and Zhu, Ke and Wu, Jianxin.*\n\n  [[Paper](https://arxiv.org/abs/2312.07856)][[Code](https://github.com/heekhero/DTL)] ![](https://img.shields.io/badge/DTL-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[7] Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions,** ACM MM 2024.\n  \n  *Yin, Dongshuo and Han, Xueting and Li, Bin and Feng, Hao and Bai, Jing.*\n\n  [[Paper](https://arxiv.org/abs/2306.09729)][Code] ![](https://img.shields.io/badge/E3VA-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[8] Ladder Fine-tuning approach for SAM integrating complementary network,** Arxiv 2023.\n  \n  *Chai, Shurong and Jain, Rahul Kumar and Teng, Shiyu and Liu, Jiaqing and Li, Yinhao and others.*\n\n  [[Paper](https://arxiv.org/abs/2306.12737)][[Code](https://github.com/11yxk/SAM-LST)] ![](https://img.shields.io/badge/SAM_LST-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[9] End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames,** CVPR 2024.\n  \n  *Liu, Shuming and Zhang, Chen-Lin and Zhao, Chen and Ghanem, Bernard.*\n\n  [[Paper](https://arxiv.org/pdf/2311.17241.pdf)] [Code] ![](https://img.shields.io/badge/AdaTAD-blue) ![](https://img.shields.io/badge/Temporal_Action_Detection-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n  \n- **[10] Time-, Memory- and Parameter-Efficient Visual Adaptation,** CVPR 2024.\n  \n  *Mercea, Otniel-Bogdan and Gritsenko, Alexey and  Schmid, Cordelia and Arnab, Anurag.*\n\n  [[Paper](https://arxiv.org/pdf/2402.02887.pdf)] [Code] ![](https://img.shields.io/badge/LoSA-blue) ![](https://img.shields.io/badge/Image_and_Video_Classification-green) ![](https://img.shields.io/badge/Param\u0026Memory\u0026Time\u0026Inference_Efficient-orange)\n\n- **[11] Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning,** ArXiv 2024.\n  \n  *Tang, Ningyuan and Fu, Minghao and Zhu, Ke and Wu, Jianxin.*\n\n  [[Paper](https://arxiv.org/pdf/2402.04009.pdf)] [Code] ![](https://img.shields.io/badge/LAST-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[12] LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization,** ArXiv 2024.\n  \n  *Gupta, Akshita and Mittal, Gaurav and Magooda, Ahmed and Yu, Ye and Taylor, Graham W and Chen, Mei.*\n\n  [[Paper](https://arxiv.org/pdf/2404.01282.pdf)] [Code] ![](https://img.shields.io/badge/LoSA-blue) ![](https://img.shields.io/badge/Temporal_Action_Localization-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)\n\n- **[13] BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation,** ICLR 2024.\n  \n  *Wang, Yaoming and Li, Jin and ZHANG, XIAOPENG and Shi, Bowen and Li, Chenglin and Dai, Wenrui and Xiong, Hongkai and Tian, Qi.*\n\n  [[Paper](https://openreview.net/pdf?id=wHLDHRkmEu)] [[Code](https://github.com/NastrondAd/BarLeRIa)] ![](https://img.shields.io/badge/BarLeRIa-blue) ![](https://img.shields.io/badge/Referring_Image_Segmentation-green) ![](https://img.shields.io/badge/Param_Efficient-orange)\n\n- **[14] UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory,** CVPR 2024.\n  \n  *Haiwen Diao, Bo Wan, Ying Zhang, Xu Jia, Huchuan Lu, Long Chen.*\n\n  [[Paper](https://arxiv.org/abs/2308.14316)] [[Code](https://github.com/Paranioar/UniPT)] ![](https://img.shields.io/badge/UniPT-blue) ![](https://img.shields.io/badge/Vision_Language_\u0026_GLUE_Task-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)  \n\n- **[15] SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning,** ECCV 2024.\n  \n  *Haiwen Diao, Bo Wan, Xu Jia, Yunzhi Zhuge, Ying Zhang, Huchuan Lu, Long Chen.*\n\n  [[Paper](https://arxiv.org/abs/2407.07523)] [[Code](https://github.com/Paranioar/SHERL)] ![](https://img.shields.io/badge/SHERL-blue) ![](https://img.shields.io/badge/Vision_Language_\u0026_GLUE_Task-green) ![](https://img.shields.io/badge/Param\u0026Memory_Efficient-orange)  \n\n\n### Partial-based Tuning\n### Specification Tuning\n- **[1] Do Better ImageNet Models Transfer Better?,** CVPR 2019.\n  \n  *Kornblith, Simon and Shlens, Jonathon and Le, Quoc V.*\n\n  [[Paper](https://openaccess.thecvf.com/content_CVPR_2019/html/Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper.html)][[Code](https://github.com/lsh3163/Imagenet-Better)]\n\n- **[2] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models.** ACL 2022.\n\n  *Zaken, Elad Ben and Ravfogel, Shauli and Goldberg, Yoav.*\n\n  [[Paper](https://arxiv.org/pdf/2106.10199.pdf)][[Code](https://github.com/benzakenelad/BitFit)] ![](https://img.shields.io/badge/BitFit-blue) ![](https://img.shields.io/badge/Bias_Tuning-orange)\n\n- **[3] Differentially Private Bias-Term only Fine-tuning of Foundation Models,** Arxiv 2022.\n  \n  *Bu, Zhiqi and Wang, Yu-Xiang and Zha, Sheng and Karypis, George.*\n\n  [[Paper](https://arxiv.org/abs/2210.00036)][Code] ![](https://img.shields.io/badge/DP_BiTFiT-blue) ![](https://img.shields.io/badge/Bias_Tuning-orange)\n  \n- **[4] AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks,** NAACL 2022.\n  \n  *Fu, Chin-Lun and Chen, Zih-Ching and Lee, Yun-Ru and Lee, Hung-yi.*\n\n  [[Paper](https://arxiv.org/abs/2205.00305)][[Code](https://github.com/Allen0307/AdapterBias)] ![](https://img.shields.io/badge/AdapterBias-blue) ![](https://img.shields.io/badge/LayerNorm_Tuning-orange)\n  \n- **[5] Strong Baselines for Parameter Efficient Few-Shot Fine-tuning,** AAAI 2024.\n  \n  *Basu, Samyadeep and Massiceti, Daniela and Hu, Shell Xu and Feizi, Soheil.*\n\n  [[Paper](https://arxiv.org/abs/2304.01917)][Code] ![](https://img.shields.io/badge/LN_TUNE-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/LayerNorm_Tuning-orange)\n\n- **[6] DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning,** ICCV 2023.\n  \n  *Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, Zhenguo Li.*\n\n  [[Paper](https://arxiv.org/abs/2304.06648)][[Code](https://github.com/mkshing/DiffFit-pytorch)] ![](https://img.shields.io/badge/DiffFit-blue) ![](https://img.shields.io/badge/Generate_Task-green) ![](https://img.shields.io/badge/Bias_Tuning-orange)\n\n- **[7] Gradient-based Parameter Selection for Efficient Fine-Tuning,** Arxiv 2023.\n  \n  *Zhi Zhang, Qizhe Zhang, Zijun Gao, Renrui Zhang, Ekaterina Shutova, Shiji Zhou, Shanghang Zhang.*\n\n  [[Paper](https://arxiv.org/abs/2312.10136)][[Code]()] ![](https://img.shields.io/badge/GPS-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Importance_Parameter_Tuning-orange)\n\n- **[8] Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning,** ICCV 2023.\n  \n  *Haoyu He, Jianfei Cai, Jing Zhang, Dacheng Tao, Bohan Zhuang.*\n\n  [[Paper](https://arxiv.org/abs/2303.08566)][[Code](https://github.com/ziplab/SPT)] ![](https://img.shields.io/badge/SPT-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Importance_Parameter_Tuning-orange)\n\n- **[9] Gradient-based Parameter Selection for Efficient Fine-Tuning,** CVPR 2024.\n  \n  *Zhi Zhang, Qizhe Zhang, Zijun Gao, Renrui Zhang, Ekaterina Shutova, Shiji Zhou, Shanghang Zhang.*\n  \n\n### Reparameter Tuning\n- **[1] LoRA: Low-Rank Adaptation of Large Language Models.** NeurIPS 2021.\n\n  *Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and others.*\n\n  [[Paper](https://arxiv.org/pdf/2106.09685.pdf)][[Code](https://github.com/microsoft/LoRA)] ![](https://img.shields.io/badge/LoRA-blue) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[2] Scaling \u0026 Shifting Your Features: A New Baseline for Efficient Model Tuning,** NeurIPS 2022.\n  \n  *Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang.*\n\n  [[Paper](https://arxiv.org/abs/2210.08823)][[Code](https://github.com/dongzelian/SSF)] ![](https://img.shields.io/badge/SSF-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/MLP_Tuning-orange)\n\n- **[3] KronA: Parameter Efficient Tuning with Kronecker Adapter,** Arxiv 2023.\n  \n  *Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J. Clark, Mehdi Rezagholizadeh.*\n\n  [[Paper](https://arxiv.org/abs/2212.10650))][Code] ![](https://img.shields.io/badge/KronA-blue) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[4] FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer,** AAAI 2023.\n  \n  *Jie, Shibo and Deng, Zhi-Hong.*\n\n  [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/25187)][Code] ![](https://img.shields.io/badge/FacT-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Tensorization_Decomposition-orange)\n\n- **[5] Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer,** Arxiv 2023.\n  \n  *Chen, Dongping.*\n\n  [[Paper](https://arxiv.org/abs/2311.06749)][[Code](https://github.com/Dongping-Chen/EFFT-EFfective-Factor-Tuning)] ![](https://img.shields.io/badge/EFFT-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Tensorization_Decomposition-orange)\n\n- **[6] Strong Baselines for Parameter Efficient Few-Shot Fine-tuning,** AAAI 2024.\n  \n  *Basu, Samyadeep and Massiceti, Daniela and Hu, Shell Xu and Feizi, Soheil.*\n\n  [[Paper](https://arxiv.org/abs/2304.01917)][Code] ![](https://img.shields.io/badge/ATTNSCALE-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[7] Parameter-efficient Model Adaptation for Vision Transformers,** AAAI 2023.\n  \n  *He, Xuehai and Li, Chunyuan and Zhang, Pengchuan and Yang, Jianwei and Wang, Xin Eric.*\n\n  [[Paper](https://arxiv.org/abs/2203.16329)][[Code](https://github.com/eric-ai-lab/PEViT)] ![](https://img.shields.io/badge/KAdaptation-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[8] DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment,** ECCV 2022.\n  \n  *Jiang, Ziyu and Chen, Tianlong and Chen, Xuxi and Cheng, Yu and Zhou, Luowei and Yuan, Lu and others.*\n\n  [[Paper](https://link.springer.com/chapter/10.1007/978-3-031-20044-1_14)][[Code](https://github.com/VITA-Group/DnA)] ![](https://img.shields.io/badge/DnA-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[9] Towards Efficient Visual Adaption via Structural Re-parameterization,** Arxiv 2023.\n  \n  *Luo, Gen and Huang, Minglang and Zhou, Yiyi and Sun, Xiaoshuai and Jiang, Guannan and Wang, Zhiyu and Ji, Rongrong.*\n\n  [[Paper](https://arxiv.org/abs/2302.08106)][[Code](https://github.com/luogen1996/RepAdapter)] ![](https://img.shields.io/badge/RepAdapter-blue) ![](https://img.shields.io/badge/Image_Classification-green) ![](https://img.shields.io/badge/Adapter_Reparameter-orange)\n\n- **[10]SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction,** AAAI 2024.\n  \n  *Zelin Peng, Zhengqin Xu, Zhilin Zeng, Xiaokang Yang, Wei Shen.*\n\n  [[Paper](https://arxiv.org/abs/2308.14604)][[Code]()] ![](https://img.shields.io/badge/SAM_PARSER-blue) ![](https://img.shields.io/badge/Dense_Prediction-green) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[10]DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models,** Arxiv 2023.\n  \n  *Shyam Marjit, Harshit Singh, Nityanand Mathur, Sayak Paul, Chia-Mu Yu, Pin-Yu Chen.*\n\n  [[Paper](https://arxiv.org/abs/2402.17412)][[Code](https://github.com/IBM/DiffuseKronA)] ![](https://img.shields.io/badge/DiffuseKronA-blue) ![](https://img.shields.io/badge/DiffusionModel-green) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n- **[11]Expanding Sparse Tuning for Low Memory Usage,** NeurIPS 2024.\n\n  *Shufan Shen, Junshu Sun, Xiangyang Ji, Qingming Huang, Shuhui Wang.*\n  \n  [[Paper](https://proceedings.neurips.cc/paper_files/paper/2024/file/8c420176b45e923cf99dee1d7356a763-Paper-Conference.pdf)][[Code](https://github.com/ssfgunner/SNELL)] ![](https://img.shields.io/badge/DiffuseKronA-blue) ![](https://img.shields.io/badge/DiffusionModel-green) ![](https://img.shields.io/badge/Weight_Tuning-orange)\n\n - **[12]PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning,** CVPR 2025.\n\n   *Song Wang, Xiaolu Liu, Lingdong Kong, Jianyun Xu, Chunyong Hu, Gongfan Fang, Wentong Li, Jianke Zhu, Xinchao Wang.*\n   \n   [[Paper](https://arxiv.org/abs/2504.16023)][[Code](https://github.com/songw-zju/PointLoRA)]\n   \n\n\n\n### Unified Tuning\n- **[1] Towards a Unified View of Parameter-Efficient Transfer Learning,** ICLR 2022.\n\n  *Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig.*\n\n  [[Paper](https://arxiv.org/abs/2110.04366)][[Code](https://github.com/jxhe/unify-parameter-efficient-tuning)] ![](https://img.shields.io/badge/Unified_View-orange)\n\n- **[2] Towards a Unified View on Visual Parameter-Efficient Transfer Learning,** Arxiv 2023.\n\n  *Yu, Bruce XB and Chang, Jianlong and Liu, Lingbo and Tian, Qi and Chen, Chang Wen.*\n\n  [[Paper](https://arxiv.org/abs/2210.00788)][[Code](https://github.com/bruceyo/V-PETL)] ![](https://img.shields.io/badge/V_PETL-blue) ![](https://img.shields.io/badge/Video_Recognition-green) ![](https://img.shields.io/badge/Unified_View-orange)\n\n- **[3] Neural Prompt Search,** Arxiv 2022.\n  \n  *Zhang, Yuanhan and Zhou, Kaiyang and Liu, Ziwei.*\n\n  [[Paper](https://arxiv.org/abs/2206.04673)][[Code](https://github.com/Davidzhangyuanhan/NOAH)] ![](https://img.shields.io/badge/NOAH-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Framework-orange)\n\n- **[4] Rethinking Efficient Tuning Methods from a Unified Perspective,** Arxiv 2023.\n  \n  *Jiang, Zeyinzi and Mao, Chaojie and Huang, Ziyuan and Lv, Yiliang and Zhao, Deli and Zhou, Jingren.*\n\n  [[Paper](https://arxiv.org/pdf/2303.00690.pdf)][Code] ![](https://img.shields.io/badge/U_Tuning-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Unified_View-orange)\n\n- **[5] A Unified Continual Learning Framework with General Parameter-Efficient Tuning,** ICCV 2023.\n  \n  *Gao, Qiankun and Zhao, Chen and Sun, Yifan and Xi, Teng and Zhang, Gang and Ghanem, Bernard and Zhang, Jian.*\n\n  [[Paper](https://arxiv.org/abs/2303.10070)][[Code](https://github.com/gqk/LAE)] ![](https://img.shields.io/badge/LAM-blue) ![](https://img.shields.io/badge/Continual_Learning-green) ![](https://img.shields.io/badge/Framework-orange)\n\n- **[6] GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction,** Arxiv 2023.\n\n  *Jiacheng Ruan, Jingsheng Gao, Mingye Xie, Suncheng Xiang, Zefang Yu, Ting Liu, Yuzhuo Fu.*\n\n  [[Paper](https://arxiv.org/pdf/2312.07255.pdf)][Code] ![](https://img.shields.io/badge/GIST-blue) ![](https://img.shields.io/badge/Image_Recognition-green) ![](https://img.shields.io/badge/Framework-orange)\n\n## 🎯 \u003cspan id=\"head1\"\u003e *Datasets of Visual PETL* \u003c/span\u003e\n| Name | Paper | Link | Notes |\n|:-----|:-----:|:----:|:-----:|\n| **FGVC** | [Visual prompt tuning](https://arxiv.org/abs/2203.12119) | [Link](https://cornell.app.box.com/v/vptfgvcsplits) | FGVC consists of 5 benchmarked Fine-Grained Visual Classification tasks. |\n| **VTAB-1k** | [A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark](https://arxiv.org/abs/1910.04867) | [Link](https://cornell.app.box.com/v/vptfgvcsplits) | VTAB-1k consists of 19 diverse visual classification tasks.|\n| **Kinetics-400** | [The kinetics human action video dataset.](https://arxiv.org/abs/1705.06950) | [Link](https://drive.google.com/file/d/11US3KptpqHsZ5K4wQLzs-OA3Y50OWtPJ/view?usp=sharing) | Video Action Recognition|\n| **SSv2** | [The “something something” Video Database for Learning and Evaluating Visual Common Sense](https://arxiv.org/abs/1706.04261) | [Link](https://developer.qualcomm.com/software/ai-datasets/something-something) | Video Action Recognition|\n| **HMDB51** | [HMDB:ALargeVideo Database for Human Motion Recognition](http://cbcl.mit.edu/publications/ps/Kuehne_etal_iccv11.pdf) | [Link](http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/) | Video Action Recognition|\n| **Diving-48** | [RESOUND: Towards Action Recognition without Representation Bias](https://openaccess.thecvf.com/content_ECCV_2018/papers/Yingwei_Li_RESOUND_Towards_Action_ECCV_2018_paper.pdf) | [Link](http://www.svcl.ucsd.edu/projects/resound/dataset.html) | Video Action Recognition|\n| **UCF-101** | [UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild](https://arxiv.org/abs/1212.0402) | [Link](https://www.crcv.ucf.edu/data/UCF101.php) | Video Action Recognition|\n| **MSCOCO** | [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312) | [Link](http://cocodataset.org/) | Instance Segmentation|\n| **ADE20K** | [Semantic Understanding of Scenes through the ADE20K Dataset](https://arxiv.org/abs/1608.05442) | [Link](http://groups.csail.mit.edu/vision/datasets/ADE20K/) | Semantic Segmentation|\n| **PASCALVOC** | [The Pascal Visual Object Classes Challenge: A Retrospective](https://www.semanticscholar.org/paper/The-Pascal-Visual-Object-Classes-Challenge%3A-A-Everingham-Eslami/616b246e332573af1f4859aa91440280774c183a) | [Link](https://host.robots.ox.ac.uk/pascal/VOC/voc2012/) | Semantic Segmentation|\n\n## 🧒 \u003cspan id=\"head1\"\u003e *Contribution* \u003c/span\u003e\n\n\u003c!-- Copy-paste in your Readme.md file --\u003e\n\n\u003ca href=\"https://github.com/synbol/Awesome-Parameter-Efficient-Transfer-Learning/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=synbol/Awesome-Parameter-Efficient-Transfer-Learning\" /\u003e\n\u003c/a\u003e\n\n### :clap: Thanks to the above contributors for this excellent work！\n\n\n## ⭐ \u003cspan id=\"head1\"\u003e *Citation* \u003c/span\u003e\n\nIf you find our survey and repository useful for your research, please cite it below:\n\n```bibtex\n\n@article{xin2024parameter,\n  title={Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey},\n  author={Xin, Yi and Luo, Siqi and Zhou, Haodi and Du, Junlong and Liu, Xiaohong and Fan, Yue and Li, Qing and Du, Yuntao},\n  journal={arXiv preprint arXiv:2402.02242},\n  year={2024}\n}\n\n```\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/synbol%2Fawesome-parameter-efficient-transfer-learning/projects"}