深度语义关联学习的基于图像视觉数据跨域检索OA北大核心CSTPCD

Image-based cross-domain retrieval of visual data is performed to identify cross-domain images and three-dimensional model data that are semantically consistent with or similar in appearance to an input image.In this task,the modal heterogeneity between cross-domain data must be addressed to achieve cross-domain correspondence between the query images and target objects.Existing methods achieve domain or semantic alignment of cross-domain features by constructing a common feature space and using a domain-adaptation or depth metric algorithm.The effectiveness of these methods has only been verified in a single type of cross-domain retrieval task.To address the above issues,a method based on deep semantic correlation learning is proposed for many types of image-based cross-domain visual-data retrieval tasks.First,heterogeneous networks are used to extract the original visual features of cross-domain data.Subsequently,a common feature space is constructed to map the original features for subsequent domain and semantic alignments.Finally,intra-modal discrimination learning,inter-modal consistency learning,and cross-modal correlation learning are performed to eliminate the heterogeneity among cross-domain features,determine the semantic relevance among cross-domain data features,and generate robust and uniform feature representations for retrieval tasks.Experimental results show that the mean Average Precision(mAP)values of this method on the TU-Berlin,IM2MN,and MI3DOR datasets are 0.448,0.689,and 0.874,respectively,significantly better than comparative methods.