Recently, adversarial-based domain adaptive object detection (DAOD) methods have been developed rapidly. However, there are two issues that need to be resolved urgently. Firstly, numerous methods reduce the distributional shifts only by aligning all the feature between the source and target domain, while ignoring the private information of each domain. Secondly, DAOD should consider the feature alignment on object existing regions in images. But redundancy of the region proposals and background noise could reduce the domain transferability. Therefore, we establish a Feature Separation and Alignment Network (FSANet) which consists of a gray-scale feature separation (GSFS) module, a local-global feature alignment (LGFA) module and a region-instance-level alignment (RILA) module. The GSFS module decomposes the distractive/shared information which is useless/useful for detection by a dual-stream framework, to focus on intrinsic feature of objects and resolve the first issue. Then, LGFA and RILA modules reduce the distributional shifts of the multi-level features. Notably, scale-space filtering is exploited to implement adaptive searching for regions to be aligned, and instance-level features in each region are refined to reduce redundancy and noise mentioned in the second issue. Various experiments on multiple benchmark datasets prove that our FSANet achieves better performance on the target domain detection and surpasses the state-of-the-art methods.
翻译:最近,基于对抗性的适应性物体探测(DAOD)方法得到迅速开发,但有两个问题需要紧急解决:第一,许多方法仅通过将源和目标领域的所有特征统一起来,而忽略每个领域的私人信息,从而减少分布变化;第二,DAOD应考虑现有目标区域图像的特征调整;但区域建议和背景噪音的冗余可以减少域的可转移性;因此,我们建立了一个特点分离和调整网络(FSANet),由灰度特征分离模块(GSFS)组成,一个地方-全球特征校准模块和一个区域-全球特征调整模块(RILLA)模块。GFS模块将分散分散分散所有特性/共享信息,而这些信息对于通过双流框架探测现有目标区域、侧重于物体的内在特征和解决第一个问题来说是毫无用处的。然后,LGFA和RIA模块减少了多级特征的分布变化。 值得注意的是,正在利用空间过滤来对区域进行适应性搜索,以便调整区域进行区域适应性搜索,从而改进我们提到的多级域域域域域域探测方法。