LAUSR

Multi-uncrewed aerial vehicle (UAV) cooperative maritime object recognition aims to maintain high accuracy under dynamic aerial perspectives for maritime search and rescue missions. Existing continual learning methods retain knowledge by approximating the global distribution of prior data but fail to address cross-perspective knowledge conflicts caused by distribution shifts across aerial perspectives, leading to gradient perturbations that harm consistency and accuracy in dynamic maritime environments. To address the issues, we propose a conflict-modulated (ConMod) generative continual learning framework, comprising generative perspective-robust conflict estimation and ConMod continual learning modules. The generative perspective-robust conflict estimation employs a perspective-aware scene generator that embeds maritime knowledge priors as perspective constraints to augment the data distribution, thereby facilitating explainable cross-perspective conflict association and promoting robust conflict index estimation. It also incorporates a dual-modal conflict index estimator that integrates geometric distortion and environmental variation branches to estimate conflict indices by associating simulated scenes with perspective-robust data distributions. Furthermore, ConMod continual learning introduces a perspective-specific triplet loss to regularize consistency by aligning geometric and environmental features within perspective-specific representation space. Additionally, a ConMod loss treats conflict indices as modulation weights to identify and reinforce representative conflict experiences from a cross-perspective memory buffer, guiding gradient updates toward global optimization. Results on the SeaDronesSee-CL and SeaDronesSee-CL-v2 datasets show that ConMod effectively improves multi-UAV cooperative maritime object recognition by identifying and mitigating cross-perspective knowledge conflicts.