In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc. Thus, for learning a general model, training with data from multiple different datasets might be a remedy and be of great value. In this paper, we resort to the multi-domain joint learning and propose a simple but effective Domain-specific Knowledge Propagating Network (DKPNet)1 for unbiasedly learning the knowledge from multiple diverse data domains at the same time. It is mainly achieved by proposing the novel Variational Attention(VA) technique for explicitly modeling the attention distributions for different domains. And as an extension to VA, Intrinsic Variational Attention(InVA) is proposed to handle the problems of over-lapped domains and sub-domains. Extensive experiments have been conducted to validate the superiority of our DKPNet over several popular datasets, including ShanghaiTech A/B, UCF-QNRF and NWPU.
翻译:在人群计数方面,由于标签困难问题,人们认为收集一个新的大型数据集是难以做到的,该数据集在密度、场景等方面都具有丰富多样的图像。 因此,为了学习一个通用模型,对来自多种不同数据集的数据进行培训可能是一种补救办法,具有巨大价值。在本文件中,我们采用多域联合学习,并提出一个简单而有效的多域特定知识促进网络(DKPNet),1 以便在同一时间公正学习来自多个不同数据领域的知识,主要通过提出新的 " 注意变化技术 " (VA),明确模拟不同领域的关注分布。作为VA的延伸,建议 " 注意变化 " (InVA)处理过度覆盖的域和次域的问题。已经进行了广泛的试验,以证实我们的DKP网络优于包括上海科技中心A/B、UCF-QRF和NWPU在内的几个流行数据集。