Lai_SnowMaster_Comprehensive_Real-world_Image_Desnowing_via_MLLM_with_Multi-Model_Feedback@CVPR2025@CVF

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#1 SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization [PDF] [Copy] [Kimi] [REL]

Authors: Jianyu Lai, Sixiang Chen, Yunlong Lin, Tian Ye, Yun Liu, Song Fei, Zhaohu Xing, Hongtao Wu, Weiming Wang, Lei Zhu

Snowfall poses a significant challenge to visual data processing, requiring specialized desnowing algorithms. However, current models often struggle with generalization due to their reliance on synthetic datasets, creating a domain gap. Evaluating real snowfall images is difficult due to the lack of ground truth. To tackle these issues, we introduce a large-scale, high-quality dataset of 10,000 annotated real snow scenes, develop a dataset with 36k preference pairs based on human expert rankings, enhance multimodal large language models' perception of snowfall images using direct preference optimization (DPO), and refine desnowing models through a mean teacher semi-supervised framework with high-quality pseudo-label screening. This Framework substantially improves the generalization and performance of desnowing models on real snowfall images.

Subject: CVPR.2025 - Poster