# IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks Source: [IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks]() Collection index: [llms.txt]() Data updated: 2026-02-28T05:00:04.461Z Authors: Yaming Zhang, Chenqiang Gao, Fangcen Liu, Junjie Guo, Lan Wang, Xinggan Peng, Deyu Meng Published: 2024-12-21T14:54:41.000Z ## Abstract Existing infrared and visible (IR-VIS) methods inherit the general representations of Pre-trained Visual Models (PVMs) to facilitate complementary learning. However, our analysis indicates that under the full fine-tuning paradigm, the feature space becomes highly constrained and low-ranked, which has been proven to seriously impair generalization. One remedy is to freeze the parameters, which preserves pretrained knowledge and helps maintain feature diversity. To this end, we propose IV-tuning, to parameter-efficiently harness PVMs for various IR-VIS downstream tasks, including salient object detection, semantic segmentation, and object detection. Extensive experiments across various settings demonstrate that IV-tuning outperforms previous state-of-the-art methods, and exhibits superior generalization and scalability. Remarkably, with only a single backbone, IV-tuning effectively facilitates the complementary learning of infrared and visible modalities with merely 3% trainable backbone parameters, and achieves superior computational efficiency compared to conventional IR-VIS paradigms. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()