Quality-Driven and Diversity-Aware Sample Expansion for Robust Marine Obstacle Segmentation
This work addresses the scarcity and lack of diversity in marine datasets for obstacle segmentation, which is an incremental improvement over existing methods.
The paper tackled the problem of robust marine obstacle segmentation under challenging conditions by proposing a quality-driven and diversity-aware sample expansion pipeline that generates synthetic training data at inference time, resulting in improved segmentation performance across multiple backbones and increased visual variation in rare and texture-sensitive classes.
Marine obstacle detection demands robust segmentation under challenging conditions, such as sun glitter, fog, and rapidly changing wave patterns. These factors degrade image quality, while the scarcity and structural repetition of marine datasets limit the diversity of available training data. Although mask-conditioned diffusion models can synthesize layout-aligned samples, they often produce low-diversity outputs when conditioned on low-entropy masks and prompts, limiting their utility for improving robustness. In this paper, we propose a quality-driven and diversity-aware sample expansion pipeline that generates training data entirely at inference time, without retraining the diffusion model. The framework combines two key components:(i) a class-aware style bank that constructs high-entropy, semantically grounded prompts, and (ii) an adaptive annealing sampler that perturbs early conditioning, while a COD-guided proportional controller regulates this perturbation to boost diversity without compromising layout fidelity. Across marine obstacle benchmarks, augmenting training data with these controlled synthetic samples consistently improves segmentation performance across multiple backbones and increases visual variation in rare and texture-sensitive classes.