用小感受野判别器提升相机图像失真域适应效率
Domain Adaptation for Camera-Specific Image Characteristics using Shallow Discriminators
- 设计浅层判别器，以小感受野精准捕捉局部失真特征
- 在实例分割任务中，平均精度最高提升0.16（相机特有失真）
- 参数量仅需主流方法1/20，适合资源受限场景
每个成像系统都会产生独特的相机特定图像特性，降低图像质量。在基于学习的感知算法中，应用阶段出现但训练数据中缺失的特性会引发域差距，影响性能。此前已有通过无配对学习建模原始图像到失真图像映射的方法。本文提出浅层判别器架构，以解决上述方法的局限性。我们证明，更小的感受野能以更低的网络复杂度更准确地再现局部失真特征。在实例分割的域适应设置下，针对单个失真类型，平均精度提升最高达0.15；针对相机特定图像特性，在简化相机模型中提升最高达0.16。在参数量方面，本方法与一种先进方法相当，但相比另一方法减少20倍复杂度，展现出卓越效率且不牺牲性能。
原文摘要 · Abstract (English)
Each image acquisition setup leads to its own camera-specific image characteristics degrading the image quality. In learning-based perception algorithms, characteristics occurring during the application phase, but absent in the training data, lead to a domain gap impeding the performance. Previously, pixel-level domain adaptation through unpaired learning of the pristine-to-distorted mapping function has been proposed. In this work, we propose shallow discriminator architectures to address limitations of these approaches. We show that a smaller receptive field size improves learning of unknown image distortions by more accurately reproducing local distortion characteristics at a low network complexity. In a domain adaptation setup for instance segmentation, we achieve mean average precision increases over previous methods of up to 0.15 for individual distortions and up to 0.16 for camera-specific image characteristics in a simplified camera model. In terms of number of parameters, our approach matches the complexity of one state of the art method while reducing complexity by a factor of 20 compared to another, demonstrating superior efficiency without compromising performance.
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