Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions must be taken. However, the surveillance domain (acquisition device, operating conditions, etc.) is often unknown, which prevents any possibility of scene-specific optimization. In this paper, we define a probabilistic framework and present a formal proof of an algorithm for the unsupervised many-to-infinity domain adaptation of posteriors. Our proposed algorithm is applicable when the probability measure associated with the target domain is a convex combination of the probability measures of the source domains. It makes use of source models and a domain discriminator model trained off-line to compute posteriors adapted on the fly to the target domain. Finally, we show the effectiveness of our algorithm for the task of semantic segmentation in real-world surveillance. The code is publicly available at https://github.com/rvandeghen/MDA.
翻译:在现实世界的监视中遇到的各种任务可以通过确定必须作出关键决定的后星体(例如,Bayesian 推断或机器学习)来解决,然而,监视领域(购置装置、操作条件等)往往不为人所知,这妨碍了任何针对具体场景的优化的可能性。在本文中,我们界定了一个概率框架,并正式证明后星体不受监督的多至无限域适应的算法。我们提议的算法适用于与目标域相关的概率计量是源域概率计量的组合。它利用源模型和经过培训的域歧视模型,对飞到目标域的后机体进行编译。最后,我们展示了我们在现实世界监视中的语系分割任务算法的有效性。该代码可在https://github.com/rvandeghen/MDA公开查阅。