NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are developed and evaluated using our collected real-world Raindrop Clarity dataset. Unlike existing deraining datasets, our Raindrop Clarity dataset is more diverse and challenging in degradation types and contents, which includes day raindrop-focused, day background-focused, night raindrop-focused, and night background-focused degradations. This dataset is divided into three subsets for competition: 14,139 images for training, 240 images for validation, and 731 images for testing. The primary objective of this challenge is to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions. There are a total of 361 participants in the competition, and 32 teams submitting valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the Raindrop Clarity dataset. The project can be found at https://lixinustc.github.io/CVPR-NTIRE2025-RainDrop-Competition.github.io/.

LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation

Low-Rank Adaptation (LoRA) introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources. But it still faces challenges of resource consumption when scaling up to larger models. Previous studies employ pruning techniques by evaluating the importance of LoRA parameters for different layers to address the problem. However, these efforts only analyzed parameter features to evaluate their importance. Indeed, the output of LoRA related to the parameters and data is the factor that directly impacts the frozen model. To this end, we propose LoRA-drop which evaluates the importance of the parameters by analyzing the LoRA output. We retain LoRA for important layers and the LoRA of the other layers share the same parameters. Abundant experiments on NLU and NLG tasks demonstrate the effectiveness of LoRA-drop.