先白平衡，后调整：通过视觉-语言评估实现跨相机颜色恒常性
White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation
AI总结：
本文提出VLM-CC框架，通过视觉-语言模型评估实现跨相机颜色恒常性的迭代反馈优化，利用感知反馈替代直接RGB回归，提升鲁棒性。
摘要：
颜色恒常性旨在保持物体颜色在不同光照下的一致性。跨相机颜色恒常性仍具挑战性，因为基于学习的模型常过拟合训练相机的颜色响应特性，导致在其他相机拍摄的图像上性能下降。我们提出VLM-CC，一种反馈引导的框架，将颜色恒常性建模为迭代细化过程。而不是直接从原始输入估计光源，VLM-CC通过视觉-语言模型（VLM）基于的评估进行迭代修正。在每次迭代中，图像使用当前估计进行白平衡并转换为伪sRGB。一个轻量级的LoRA微调VLM然后评估校正后的图像，识别主导的残差色偏并提供定性反馈。此反馈被映射到残差照明方向（红、绿或蓝）并用于更新光源估计，直到收敛。我们的关键思想是将颜色恒常性重新建模为迭代感知反馈问题，利用VLM评估而不是直接RGB回归。通过将直接RGB估计替换为VLM引导的感知反馈，VLM-CC在多个数据集上实现了跨相机颜色恒常性的最先进鲁棒性。代码将在https://github.com/NothingIknow/VLM-CC上提供。
英文摘要：
Color constancy aims to keep object colors consistent under varying illumination. Cross-camera generalization in color constancy remains challenging because learning-based models often overfit to the color response characteristics of the training camera, resulting in degraded performance on images captured by other cameras. We propose VLM-CC, a feedback-guided framework that formulates color constancy as an iterative refinement process. Instead of directly estimating the illuminant from raw input, VLM-CC performs iterative correction driven by vision-language model (VLM)-based evaluation. At each iteration, the image is white-balanced using the current estimate and converted to pseudo-sRGB. A lightweight LoRA-tuned VLM then assesses the corrected image, identifying the dominant residual color cast and providing qualitative feedback. This feedback is mapped to a residual illumination direction (red, green, or blue) and used to update the illuminant estimate until convergence. Our key idea is to reframe color constancy as an iterative perceptual feedback problem, leveraging VLM evaluation instead of direct RGB regression. By replacing direct RGB estimation with VLM-guided perceptual feedback, VLM-CC achieves state-of-the-art robustness in cross-camera color constancy across multiple datasets. Code will be available at https://github.com/NothingIknow/VLM-CC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
