Adapting foundation models to a new task within or beyond the training domain is the de facto standard for building specialized machine learning models. Recently, Visual Prompting (VP) has emerged as...
arXiv:2505.07675v3 Announce Type: replace-cross
Abstract: Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Rece...
By Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Sung Ju Hwang
ES‑VP introduces Energy‑Shaped Visual Prompting, a method that generates image‑specific prompts through low‑rank initialization and energy‑guided dynamic adaptation. It achieves higher performance than existing single‑prompt and diverse‑prompt approaches while using far fewer parameters. Experiments on five architectures and fifteen datasets show consistent superiority, including a 2.6% accuracy gain over DAM‑VP on CLIP with 590× fewer prompt parameters.
By Can Jin, Ying Li, Jingchen Sun, Hongwu Peng, Jiahui Zhao, Yang Zhou, Lei Li, Dimitris N. Metaxas
The paper introduces Mask Fine‑Tuning (MFT), a new approach for adapting Vision‑Language Models that avoids modifying backbone weights. MFT learns masks to selectively route information through existing pretrained connections, dynamically uncovering subnetworks that better align with downstream tasks. Experiments demonstrate that MFT consistently outperforms both Full Fine‑Tuning and Parameter‑Efficient Fine‑Tuning across multiple benchmarks, while also offering insights into how pretrained VLMs reorganize their internal pathways during adaptation.
By Mingyuan Zhang, Yue Bai, Yifan Wang, Yiyang Huang, Yun Fu
arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
By Jianlu Shen, Fu Feng, Yucheng Xie, Jiaqi Lv, Xin Geng
arXiv:2607. 28967v1 Announce Type: cross Abstract: Prompt tuning adapts vision--language models with few trainable parameters, but existing approaches trade off efficiency and adaptation: static textual prompts can overfit source classes, image-conditioned prompts add per-instance computation, and multimodal tuning modifies the visual branch.
By Pouya Parsa, Raoof Zare Moayedi, Seongjin Choi