arXiv 2605.117712026-06-23cs.CV

重新审视从视觉-语言视角的阴影检测

Revisiting Shadow Detection from a Vision-Language Perspective

CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Department of Electronic Engineering and Information Science, University of Science and Technology of China（中国科学技术大学地球空间信息处理与应用系统重点实验室，电子工程与信息科学系）

Yonghui Wang, Shaokai Liu, Wengang Zhou, Hao Feng, Houqiang Li

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AI总结：

本文从视觉-语言视角重新审视阴影检测，提出SVL框架，利用语言作为语义参考以区分相似的暗区，通过全局阴影比例回归和全局-局部耦合机制提升鲁棒性。

摘要：

阴影检测通常被建模为视觉驱动的密集预测问题，模型主要依赖像素级视觉监督区分阴影与非阴影区域。然而，在视觉模糊情况下，相似的暗区可能对应阴影或固有暗表面，仅靠视觉证据不足以建立稳定的决策规则。本文从视觉-语言视角重新审视阴影检测，认为稳健预测需要超越视觉线索的显式语义参考。我们提出SVL框架，利用语言作为显式语义参考以区分视觉相似的暗区。SVL通过场景级阴影比例回归目标对全局图像表示与阴影相关文本嵌入进行对齐，从而为图像级提供关于阴影总体范围的指导。为了将此全局指导转移到密集推理，SVL引入全局到局部的耦合机制，强制图像级指导与补丁级预测之间的一致性。同时，SVL应用局部补丁级约束与文本嵌入以在具有挑战性的外观条件下提升细粒度辨别能力。基于冻结的DINOv3图像编码器，该框架仅学习轻量级的投影和解码模块，实现参数高效的设计，训练参数少于1%。在多个阴影检测基准上的广泛实验，包括专门的困难案例评估，表明整体性能强劲，并在视觉模糊条件下提高了鲁棒性。

英文摘要：

Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to distinguish shadows from non-shadow regions. However, this formulation can become unreliable in visually ambiguous cases, where similar dark regions may correspond either to cast shadows or to intrinsically dark surfaces, making visual evidence alone insufficient for establishing a stable decision rule. In this work, we revisit shadow detection from a vision--language perspective and argue that robust prediction benefits from an explicit semantic reference beyond visual cues alone. We propose SVL, a Shadow Vision--Language framework that uses language as an explicit semantic reference to disambiguate shadows from visually similar dark regions. SVL aligns global image representations with shadow-related text embeddings through scene-level shadow ratio regression, and transfers this semantic guidance to dense prediction via global-to-local coupling and local patch-level constraints. Built on a frozen DINOv3 image encoder, SVL learns only lightweight projection and decoding modules, yielding a parameter-efficient design with less than $1\%$ trainable parameters. Extensive experiments on multiple shadow detection benchmarks, including dedicated hard-case evaluations, suggest strong overall performance and improved robustness under visually ambiguous conditions. Code is available at https://github.com/harrytea/SVL.

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