Progressively Alleviating Noise for Unsupervised Cross-Domain Image Retrieval

Unsupervised cross-domain image retrieval (UCDIR) aims to obtain images with the same category from other domains yet without labels for all data. The prevailing strategy first enhances the intra-domain representation by clustering, and then realizes cross-domain alignment based on various similarity comparisons. Despite their significant advancements, current methods are hindered by substantial ambiguity resulting from the absence of explicit semantic information, exacerbated by various sources of noise such as background interference and attribute composition. This difficulty can compromise the performance of existing clustering-based methods. Therefore, we propose a straightforward yet effective strategy to Progressively Alleviate Noise (termedPAN) for UCDIR. We initially utilize information entropy to quantify the certainty of prediction, which also serves as an indicator of uncertainty level resulting from noise. We further refine a progressive learning approach that specifically targets data with low confidence to enhance the semantic comprehension. The model can gradually focus on data with appropriate uncertainty for semantic learning and cross-domain alignment via dynamic reweighing, thereby excluding the adverse effects of noisy data in an incremental manner. Extensive experiments on three widely used datasets for UCDIR demonstrate our effectiveness and superiority.