BOCCHI：一个使用MSDCT-UNet进行局部运动模糊检测的更现实且具挑战性的基准
BOCCHI: A More Realistic and Challenging Benchmark for Local Motion Blur Detection with MSDCT-UNet
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中文总结 AI 辅助
研究局部运动模糊检测问题，提出MSDCT-UNet方法，通过DCT注意力和FiLM注入多尺度DCT先验，引入BOCCHI基准，该方法在BOCCHI上表现优异，且经其训练的模型在跨数据集迁移中表现出色。
中文摘要 AI 辅助
局部运动模糊检测需要对模糊区域进行像素级定位。现有基准使模型依赖无法迁移的梯度捷径。我们引入了BOCCHI（通过人工标注图像跨相机捕获的模糊对象），这是一个真实捕获的基准，其清晰区域与模糊梯度分布重叠并克服了这些捷径，并提出了MSDCT-UNet（多尺度离散余弦变换UNet），一种通过DCT注意力和FiLM注入多尺度DCT先验的频率感知编码器-解码器。MSDCT-UNet在BOCCHI的域内mIoU和边界定位中排名第一，并且仅用633张训练图像，经BOCCHI训练的模型在跨数据集迁移上优于其他训练源。
英文摘要
Local motion blur detection requires pixel-level localization of blurred regions. Existing benchmarks let models rely on gradient shortcuts that fail to transfer. We introduce BOCCHI (Blurred Objects Captured across Cameras with Human-annotated Imagery), a real-captured benchmark whose sharp regions overlap the blur gradient distribution and defeat these shortcuts, and propose MSDCT-UNet (Multi-Scale Discrete Cosine Transform UNet), a frequency-aware encoder-decoder injecting multi-scale DCT priors through DCT Attention and FiLM. MSDCT-UNet ranks first in in-domain mIoU and boundary localization on BOCCHI, and BOCCHI-trained models outperform every other training source on cross-dataset transfer with only 633 training images. Our project page is available at https://brianchen1120.github.io/project/bocchi/.
