用语言和窗口控制，实现精准图像分割
LawDIS: Language-Window-based Controllable Dichotomous Image Segmentation
- 通过语言提示生成初始掩码，再用可调窗口精细优化
- 在DIS5K上比第二名模型高4.6%的Fβ^ω指标
- 适合需要高精度、个性化分割的场景
我们提出LawDIS，一种基于语言窗口的可控二元图像分割框架，可生成高质量物体掩码。该框架将分割任务重构为潜在扩散模型中的图像条件掩码生成，支持用户控制的无缝集成。LawDIS引入宏观与微观双控模式：宏观模式下，语言控制分割策略（LS）根据用户语言提示生成初始掩码；微观模式下，窗口控制细化策略（WR）允许对初始掩码中用户定义区域（尺寸可调窗口）进行灵活细化。通过模式切换器协调，两种模式可独立或联合运行，适用于高精度个性化应用。在DIS5K基准上的大量实验表明，LawDIS在所有指标上显著优于11种前沿方法。相较于表现第二的MVANet，结合LS与WR策略时，DIS-TE上取得4.6%的Fβ^ω提升，仅使用LS策略时也达3.6%提升。代码将开源于https://github.com/XinyuYanTJU/LawDIS。
原文摘要 · Abstract (English)
We present LawDIS, a language-window-based controllable dichotomous image segmentation (DIS) framework that produces high-quality object masks. Our framework recasts DIS as an image-conditioned mask generation task within a latent diffusion model, enabling seamless integration of user controls. LawDIS is enhanced with macro-to-micro control modes. Specifically, in macro mode, we introduce a language-controlled segmentation strategy (LS) to generate an initial mask based on user-provided language prompts. In micro mode, a window-controlled refinement strategy (WR) allows flexible refinement of user-defined regions (i.e., size-adjustable windows) within the initial mask. Coordinated by a mode switcher, these modes can operate independently or jointly, making the framework well-suited for high-accuracy, personalised applications. Extensive experiments on the DIS5K benchmark reveal that our LawDIS significantly outperforms 11 cutting-edge methods across all metrics. Notably, compared to the second-best model MVANet, we achieve $F_β^ω$ gains of 4.6\% with both the LS and WR strategies and 3.6\% gains with only the LS strategy on DIS-TE. Codes will be made available at https://github.com/XinyuYanTJU/LawDIS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。