IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks

• URL: http://arxiv.org/abs/2412.16654v3
• Date: Sat, 02 Aug 2025 09:33:14 GMT
• Title: IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks
• Authors: Yaming Zhang, Chenqiang Gao, Fangcen Liu, Junjie Guo, Lan Wang, Xinggan Peng, Deyu Meng,
• Abstract summary: Under the full fine-tuning paradigm, the feature space becomes highly constrained and low-ranked, which has been proven to seriously impair generalization.<n>We propose IV-tuning, to parameter-efficiently harness PVMs for various IR-VIS downstream tasks, including salient object detection, semantic segmentation, and object detection.<n>Compared with the full fine-tuning baselines and existing IR-VIS methods, IV-tuning facilitates the learning of complementary information between infrared and visible modalities with less than 3% of the backbone parameters.
• Score: 47.08388430506686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 solution is freezing parameters to preserve pre-trained knowledge and thus maintain diversity of the feature space. 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. Compared with the full fine-tuning baselines and existing IR-VIS methods, IV-tuning facilitates the learning of complementary information between infrared and visible modalities with less than 3% of the backbone parameters, and effectively alleviates the overfitting problem. The code is available in https://github.com/Yummy198913/IV-tuning.

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