基于渐进多源域迁移的无监督跨域目标检测

针对目标检测任务中获取人工标注训练样本的困难, 提出一种在像素级与特征级渐进完成域自适应的无监督跨域目标检测方法. 现有的像素级域自适应方法中, 存在翻译图像风格单一、内容结构不一致的问题. 因此, 将输入图像分解为域不变的内容空间及域特有的属性空间, 综合不同空间表示进行多样性的图像翻译, 同时保留图像的空间语义结构以实现标注信息的迁移. 此外, 对特征级域自适应而言, 为缓解单源域引起的源域偏向问题, 将得到的带有标注的多样性翻译图像作为多源域训练集, 设计基于多领域的对抗判别模块, 从而获取多个领域不变的特征表示. 最后, 采用自训练方案迭代生成目标域训练集伪标签, 以进一步提升模型在目标域上的检测效果. 在Cityscapes & Foggy Cityscapes与VOC07 & Clipart1k数据集上的实验结果表明, 相比现有的无监督跨域检测算法, 该检测框架具更优越的迁移检测性能.

Unsupervised Cross-domain Object Detection Based on Progressive Multi-source Transfer

To address the difficulty of collecting manually labeled training samples for object detection tasks, this paper proposes an unsupervised cross-domain object detection method that gradually adapts the model at pixel level and feature level. The existing pixel-level domain adaptive methods generate translated images with a single style and inconsistent content structure. To solve this problem, this paper embeds the input images into domain-invariant content space and domain-specific attribute space, then cooperates different space representations to synthesize diverse translated images that preserve the spatial semantic information to enable label transfer. In addition, for feature-level domain adaptation, to alleviate the source-bias problem caused by single source domain, we treat the generated diverse labeled images as source domain data and design a multi-domain discriminator to get multi-domain-invariant representations. Finally, To further enhance the detection performance on the target domain, we propose a self-training framework to alternatively generate pseudo labels on target training data. The exploratory experiment results from the Cityscapes & Foggy Cityscapes dataset and VOC07 & Clipart1k dataset demonstrate that compared with the current unsupervised cross-domain detection methods, the proposed detection framework achieves better transferability.