Image retrieval using unsupervised prompt learning and regional attention

Identifying the target object in an image can produce more accurate and discriminative feature representations, which can significantly improve large-scale instance-level image-retrieval performance. However, it is usually difficult to obtain annotation information for all target objects in a dataset manually, which makes it challenging to automatically identify target objects. To address this issue, we propose a novel method of instance-level image retrieval based on unsupervised prompt learning and regional attention (PLRA) rather than manual annotation. It includes three main components: (1) We propose an unsupervised prompt learning method to identify an image's target object. It reconstructs deep features by mining prompt information, then designs prompt factors to identify the target object based on the reconstructed features. (2) We propose a new regional attention method to extract the distinguishing features of the target object. This method captures important feature regions in four dimensions: global, local, spatial, and channel, which improves the diversity and discriminability of the representation. (3) We propose a general hybrid PCA-whitening (HPW) method based on multi-parameter learning and feature fusion, which trades off feature dimensionality with retrieval performance. This method significantly improves performance and reduces vector dimensionality in a plug-and-play manner. Comprehensive experiments on five benchmark datasets show that the proposed method significantly outperforms existing state-of-theart methods based on unsupervised feature aggregation.