We rethink the role of positional encoding in 3D representation learning and fine-tuning. We argue that using positional encoding in point Transformer-based methods serves to aggregate multi-scale features of point clouds. Additionally, we explore parameter-efficient fine-tuning (PEFT) through the lens of prompts and adapters, introducing a straightforward yet effective method called PPT for point cloud analysis. PPT incorporates increased patch tokens and trainable positional encoding while keeping most pre-trained model parameters frozen. Extensive experiments validate that PPT is both effective and efficient. Our proposed method of PEFT tasks, namely PPT, with only 1.05M of parameters for training, gets state-of-the-art results in several mainstream datasets, such as 95.01% accuracy in the ScanObjectNN OBJ_BG dataset. Codes and weights will be released at https://github.com/zsc000722/PPT.
Prompt When the Animal is: Temporal Animal Behavior Grounding with Positional Recovery Training
School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；National Key Laboratory of General Artificial Intelligence, Shenzhen Graduate School, Peking University（北京大学深圳研究生院国家通用人工智能实验室）
Pro-tuning: Unified Prompt Tuning for Vision Tasks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Huawei Cloud & AI（华为云与计算BG）；CAS Centre for Artificial Intelligence and Robotics, HK Institute of Science and Innovation（中国科学院香港创新研究院人工智能与机器人创新中心）
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
Zhejiang University（浙江大学）；The Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）
Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Microsoft Research Asia（微软亚洲研究院）；School of Telecommunication and Engineering, Xidian University（西安电子科技大学电信与工程学院）
MetaTPT: Meta Test-time Prompt Tuning for Vision-Language Models
UCAS-Terminus AI Lab, University of Chinese Academic of Sciences（中国科学院大学Terminus AI实验室，中国科学院大学）；AIM Lab, University of Amsterdam（阿姆斯特丹大学AIM实验室）；Jarvis Research Center, Tencent Youtu Lab（腾讯优图实验室 Jarvis 研究中心）；Central Research Institue, United Imaging Healthcare Co., Ltd（联合影像医疗科技有限公司中央研究所）
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Northwestern Polytechnical University（西北工业大学）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Xidian University（西安电子科技大学）
Guizhou University, Guiyang, China（贵州大学）；Harbin Institute of Technology, China（哈尔滨工业大学）；The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））
Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers
Department of Computational and Data Sciences, Indian Institute of Science（计算与数据科学系，印度科学研究院）；Accenture, Japan（日本Accenture公司）；Google, India（印度Google公司）；Indian Institute of Science（印度科学研究院）
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation
The Chinese University of Hong Kong（香港中文大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hupan Lab（湖畔实验室）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Southern University of Science and Technology（南方科技大学）
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Tulane University（路易斯安那州立大学）；Carnegie Mellon University（卡内基梅隆大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Georgia Institute of Technology（佐治亚理工学院）
MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering
Khoury College of Computer Sciences, Northeastern University（东北大学克劳尔计算机科学学院）；New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新实验室）
Prompt Your Brain: Scaffold Prompt Tuning for Efficient Adaptation of fMRI Pre-trained Model
Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院医学系）；NUS Graduate School, National University of Singapore（新加坡国立大学研究生院）
Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data
Institute of AI Innovation and Societal Impact, Hanoi University of Science and Technology（人工智能创新与社会影响研究院，河内科学技术大学）；EPFL（瑞士联邦理工学院）；Griffith University（格里菲斯大学）；Washington State University（华盛顿州立大学）
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
National Key Laboratory of Radar Signal Processing, Xidian University（西安电子科技大学雷达信号处理国家重点实验室）；McCombs School of Business, The University of Texas at Austin（德克萨斯大学奥斯汀分校麦库姆斯商学院）
Historical Test-time Prompt Tuning for Vision Foundation Models
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）
