CARP: cloud-adaptive robust prompting of vision-language models for ship classification under cloud occlusion - Zhan, Haoke; Song, Yiping; Huang, Xun; Tan, Xiao; Zhang, Ting (Frontiers in Remote Sensing, 2025)
Fine-grained few-shot ship classification under cloud occlusion is vital for maritime safety but remains challenging due to corrupted features and limited data utility. While the advent of large pre-trained vision-language models (VLMs) provides promising solutions, the lack of specialized benchmarks hinders their effective application. To address this, we introduce SeaCloud-Ship, the first benchmark dedicated to this task. It comprises 7,654 high-resolution, high-quality annotated images across 30 classes, featuring quantified cloud coverage (12.5%–75%) for standardized evaluation. We innovatively propose CARP, a cloud-aware prompting framework built upon CoOp, to combat feature corruption, semantic misalignment, and utility decay. Our core contributions include: (1) GCE Loss dynamically adjusting classification weights to suppress cloud interference based on feature degradation severity; (2) Adaptive Optimization Prompt Design (AOPD) utilizing distortion-aware vectors for effective multi-modal feature alignment and semantic deviation repair; (3) Dynamic Weight Adjustment Mechanism (DWAM) real-time balancing of multi-source feature fusion by evaluating inter-modal information gain. Extensive experiments on SeaCloud-Ship demonstrate CARP’s superior robustness and state-of-the-art performance, establishing a strong baseline for cloud-occluded ship classification.