1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

• URL: http://arxiv.org/abs/2602.10513v1
• Date: Wed, 11 Feb 2026 04:26:52 GMT
• Title: 1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization
• Authors: Dongshuo Yin, Xue Yang, Deng-Ping Fan, Shi-Min Hu,
• Abstract summary: We propose an adapter with Complex Linear Projection Optimization (CoLin) for vision tasks.<n>For architecture, we design a novel low-rank complex adapter that introduces only about 1% parameters to the backbone.<n>For efficiency, we theoretically prove that low-rank composite matrices suffer from severe convergence issues during training, and address this challenge with a tailored loss.
• Score: 34.782932303597825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying vision foundation models typically relies on efficient adaptation strategies, whereas conventional full fine-tuning suffers from prohibitive costs and low efficiency. While delta-tuning has proven effective in boosting the performance and efficiency of LLMs during adaptation, its advantages cannot be directly transferred to the fine-tuning pipeline of vision foundation models. To push the boundaries of adaptation efficiency for vision tasks, we propose an adapter with Complex Linear Projection Optimization (CoLin). For architecture, we design a novel low-rank complex adapter that introduces only about 1% parameters to the backbone. For efficiency, we theoretically prove that low-rank composite matrices suffer from severe convergence issues during training, and address this challenge with a tailored loss. Extensive experiments on object detection, segmentation, image classification, and rotated object detection (remote sensing scenario) demonstrate that CoLin outperforms both full fine-tuning and classical delta-tuning approaches with merely 1% parameters for the first time, providing a novel and efficient solution for deployment of vision foundation models. We release the code on https://github.com/DongshuoYin/CoLin.

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