Knowledge-Aided Momentum Contrastive Learning for Remote-Sensing Image Text Retrieval

Remote sensing image-text retrieval (RSITR) has attracted widespread attention due to its great potential for rapid information mining ability on remote sensing images. Although significant progress has been achieved, existing methods typically overlook the challenge posed by the extremely analogous descriptions, where the subtle differences remain largely unexploited or, in some cases, are entirely disregarded. To address the limitation, we propose a Knowledge Aided Momentum Contrastive Learning (KAMCL) method for RSITR. Specifically, we propose a novel Knowledge Aided Learning framework, including knowledge initialization, construction, filtration, and alignment operations, which aims at providing valuable concepts and learning discriminative representations. On this basis, we integrate Momentum Contrastive Learning to promote the capture of key concepts within the representation via expanding the scale of negative sample pairs. Moreover, we design a hierarchical aggregator module to better capture the multi-level information from remote sensing images. Finally, we introduce an innovative two-step training strategy designed to effectively harness the synergy among concepts and leverage their respective functionalities. Extensive experiments conducted on the three public datasets showcase the remarkable performance of our approach in terms of retrieval accuracy and computational efficiency. For instance, compared with the existing state-of-the-art method, our method exhibits notable performance improvements of 2.65% on the RSICD dataset, simultaneously achieving improvements in inference efficiency by 48%. Our source code will be released at https://github.com/mcx-mcx/KAMCL.