All You Need is One: Capsule Prompt Tuning with a Single Vector
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Yiyang, Liang, James C., Fan, Heng, Yang, Wenhao, Cui, Yiming, Han, Xiaotian, Huang, Lifu, Liu, Dongfang, Wang, Qifan, Han, Cheng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
by: Han, Cheng, et al.
Published: (2024)
by: Han, Cheng, et al.
Published: (2024)
M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
by: Wang, Taowen, et al.
Published: (2024)
by: Wang, Taowen, et al.
Published: (2024)
Visual Fourier Prompt Tuning
by: Zeng, Runjia, et al.
Published: (2024)
by: Zeng, Runjia, et al.
Published: (2024)
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
by: Zeng, Runjia, et al.
Published: (2025)
by: Zeng, Runjia, et al.
Published: (2025)
Re-Imagining Multimodal Instruction Tuning: A Representation View
by: Liu, Yiyang, et al.
Published: (2025)
by: Liu, Yiyang, et al.
Published: (2025)
Uni-LoRA: One Vector is All You Need
by: Li, Kaiyang, et al.
Published: (2025)
by: Li, Kaiyang, et al.
Published: (2025)
SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving
by: Cui, Yiming, et al.
Published: (2024)
by: Cui, Yiming, et al.
Published: (2024)
A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
by: Liu, Changyu, et al.
Published: (2026)
by: Liu, Changyu, et al.
Published: (2026)
TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching
by: Zeng, Runjia, et al.
Published: (2026)
by: Zeng, Runjia, et al.
Published: (2026)
Markov Renewal Proportional Hazards is All You Need
by: Cui, Elvis Han
Published: (2025)
by: Cui, Elvis Han
Published: (2025)
On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning
by: Liu, Changyu, et al.
Published: (2026)
by: Liu, Changyu, et al.
Published: (2026)
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
by: Cheng, Zhiyuan, et al.
Published: (2024)
by: Cheng, Zhiyuan, et al.
Published: (2024)
ProMotion: Prototypes As Motion Learners
by: Lu, Yawen, et al.
Published: (2024)
by: Lu, Yawen, et al.
Published: (2024)
Error-driven Data-efficient Large Multimodal Model Tuning
by: Yao, Barry Menglong, et al.
Published: (2024)
by: Yao, Barry Menglong, et al.
Published: (2024)
Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis
by: Zhang, Letian, et al.
Published: (2025)
by: Zhang, Letian, et al.
Published: (2025)
One Snapshot is All You Need: A Generalized Method for mmWave Signal Generation
by: Huang, Teng, et al.
Published: (2025)
by: Huang, Teng, et al.
Published: (2025)
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need
by: Wistuba, Martin, et al.
Published: (2024)
by: Wistuba, Martin, et al.
Published: (2024)
Probabilistic Token Alignment for Large Language Model Fusion
by: Zeng, Runjia, et al.
Published: (2025)
by: Zeng, Runjia, et al.
Published: (2025)
VicaSplat: A Single Run is All You Need for 3D Gaussian Splatting and Camera Estimation from Unposed Video Frames
by: Li, Zhiqi, et al.
Published: (2025)
by: Li, Zhiqi, et al.
Published: (2025)
Multimodal Instruction Tuning with Conditional Mixture of LoRA
by: Shen, Ying, et al.
Published: (2024)
by: Shen, Ying, et al.
Published: (2024)
AMD: Automatic Multi-step Distillation of Large-scale Vision Models
by: Han, Cheng, et al.
Published: (2024)
by: Han, Cheng, et al.
Published: (2024)
ParameterNet: Parameters Are All You Need
by: Han, Kai, et al.
Published: (2023)
by: Han, Kai, et al.
Published: (2023)
You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image
by: Wang, Taoyue, et al.
Published: (2026)
by: Wang, Taoyue, et al.
Published: (2026)
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
by: Wang, Taowen, et al.
Published: (2024)
by: Wang, Taowen, et al.
Published: (2024)
Top-$nσ$: Not All Logits Are You Need
by: Tang, Chenxia, et al.
Published: (2024)
by: Tang, Chenxia, et al.
Published: (2024)
RoboSeek: You Need to Interact with Your Objects
by: Peng, Yibo, et al.
Published: (2025)
by: Peng, Yibo, et al.
Published: (2025)
SMPL Normal Map Is All You Need for Single-view Textured Human Reconstruction
by: Shen, Wenhao, et al.
Published: (2025)
by: Shen, Wenhao, et al.
Published: (2025)
COCO is "ALL'' You Need for Visual Instruction Fine-tuning
by: Han, Xiaotian, et al.
Published: (2024)
by: Han, Xiaotian, et al.
Published: (2024)
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
by: Tu, Lifu, et al.
Published: (2023)
by: Tu, Lifu, et al.
Published: (2023)
Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need
by: Cui, Yongchuan, et al.
Published: (2025)
by: Cui, Yongchuan, et al.
Published: (2025)
RAE: A Neural Network Dimensionality Reduction Method for Nearest Neighbors Preservation in Vector Search
by: Zhang, Han, et al.
Published: (2025)
by: Zhang, Han, et al.
Published: (2025)
Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?
by: Bhagwatkar, Rishika, et al.
Published: (2025)
by: Bhagwatkar, Rishika, et al.
Published: (2025)
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
by: Martinka, Jakub, et al.
Published: (2025)
by: Martinka, Jakub, et al.
Published: (2025)
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
by: Wang, Bohao, et al.
Published: (2025)
by: Wang, Bohao, et al.
Published: (2025)
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
by: Bai, Yuelin, et al.
Published: (2024)
by: Bai, Yuelin, et al.
Published: (2024)
Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Need
by: Liu, Yang, et al.
Published: (2025)
by: Liu, Yang, et al.
Published: (2025)
Attention is All You Need to Defend Against Indirect Prompt Injection Attacks in LLMs
by: Zhong, Yinan, et al.
Published: (2025)
by: Zhong, Yinan, et al.
Published: (2025)
Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?
by: Xie, Guanwen, et al.
Published: (2024)
by: Xie, Guanwen, et al.
Published: (2024)
Ideal Registration? Segmentation is All You Need
by: Chen, Xiang, et al.
Published: (2025)
by: Chen, Xiang, et al.
Published: (2025)
CAMformer: Associative Memory is All You Need
by: Molom-Ochir, Tergel, et al.
Published: (2025)
by: Molom-Ochir, Tergel, et al.
Published: (2025)
Similar Items
-
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
by: Han, Cheng, et al.
Published: (2024) -
M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
by: Wang, Taowen, et al.
Published: (2024) -
Visual Fourier Prompt Tuning
by: Zeng, Runjia, et al.
Published: (2024) -
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
by: Zeng, Runjia, et al.
Published: (2025) -
Re-Imagining Multimodal Instruction Tuning: A Representation View
by: Liu, Yiyang, et al.
Published: (2025)