Domain Adaptation from Generated Multi-Weather Images for Unsupervised Maritime Object Classification

The recent study on unsupervised maritime object classification highlights the importance of accurate classification for maritime safety. By constructing the AIMO dataset with diverse weather conditions and balanced object categories, researchers have tackled the long-tail data distribution issues present in real-world datasets like RMO. The proposed domain adaptation method leverages the generative capabilities of AIMO to enhance the generalization of features using Vision-Language Models such as CLIP. This innovative approach not only improves classification accuracy but also focuses on rare object categories and challenging weather conditions, demonstrating its effectiveness through experimental results. The advancements in this field are crucial for monitoring and predicting intelligent sea environments, ultimately contributing to safer maritime operations.