基于掩码保留属性编辑的分割模型基准测试
Benchmarking Segmentation Models with Mask-Preserved Attribute Editing
AI总结：
针对现有分割模型评估仅考虑全局属性变化的局限，提出掩码保留属性编辑流水线，构建涵盖局部与全局属性变化的基准，发现两类属性均影响分割性能，强调局部属性在鲁棒性评估中的重要性。
摘要：
在实际部署分割模型时，评估其在多样复杂场景下的表现至关重要。与以往仅考虑全局属性变化（如恶劣天气）的评估范式不同，我们同时研究局部和全局属性变化以进行鲁棒性评估。为此，我们构建了一套掩码保留属性编辑流水线，可在精确控制结构信息的前提下编辑真实图像的视觉属性，从而使原始分割标签可复用于编辑后的图像。借助该流水线，我们构建了一个涵盖物体属性与图像属性（如颜色、材质、图案、风格）的基准。我们评估了多种语义分割模型，涵盖传统闭集模型到最新的开放词汇大模型，分析其对不同类型变化的鲁棒性。研究发现，局部和全局属性变化均会影响分割性能，且模型对不同变化类型的敏感度存在差异。我们认为局部属性与全局属性同等重要，在分割模型的鲁棒性评估中应予以考虑。代码详见：https://github.com/PRIS-CV/Pascal-EA。
英文摘要：
When deploying segmentation models in practice, it is critical to evaluate their behaviors in varied and complex scenes. Different from the previous evaluation paradigms only in consideration of global attribute variations (e.g. adverse weather), we investigate both local and global attribute variations for robustness evaluation. To achieve this, we construct a mask-preserved attribute editing pipeline to edit visual attributes of real images with precise control of structural information. Therefore, the original segmentation labels can be reused for the edited images. Using our pipeline, we construct a benchmark covering both object and image attributes (e.g. color, material, pattern, style). We evaluate a broad variety of semantic segmentation models, spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations. We find that both local and global attribute variations affect segmentation performances, and the sensitivity of models diverges across different variation types. We argue that local attributes have the same importance as global attributes, and should be considered in the robustness evaluation of segmentation models. Code: https://github.com/PRIS-CV/Pascal-EA.
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