针对水下图像模糊、变色问题，提出高效增强分割精度的新框架。
BARIS: Boundary-Aware Refinement with Environmental Degradation Priors for Robust Underwater Instance Segmentation
- 引入边界感知特征精炼机制，提升分割细节
- 在多个数据集上比Mask R-CNN高3.4~3.8 mAP
- 仅需原参数量10%以下，适合资源受限场景
水下实例分割因光照衰减、散射和色彩失真等视觉退化条件而极具挑战。本文提出BARIS-Decoder（边界感知精炼解码器），通过特征精炼提升分割精度。为应对水下退化，设计环境鲁棒适配器（ERA），在参数量减少90%以上的情况下有效建模退化模式。将BARIS-Decoder与ERA微调结合的BARIS-ERA框架，在Swin-B和ConvNeXt V2骨干网络下分别较Mask R-CNN提升3.4 mAP和3.8 mAP，达到当前最优性能，验证了其在水下实例分割中的鲁棒性与高效性。
原文摘要 · Abstract (English)
Underwater instance segmentation is challenging due to adverse visual conditions such as light attenuation, scattering, and color distortion, which degrade model performance. In this work, we propose BARIS-Decoder (Boundary-Aware Refinement Decoder for Instance Segmentation), a framework that enhances segmentation accuracy through feature refinement. To address underwater degradations, we introduce the Environmental Robust Adapter (ERA), which efficiently models underwater degradation patterns while reducing trainable parameters by over 90\% compared to full fine-tuning. The integration of BARIS-Decoder with ERA-tuning, referred to as BARIS-ERA, achieves state-of-the-art performance, surpassing Mask R-CNN by 3.4 mAP with a Swin-B backbone and 3.8 mAP with ConvNeXt V2. Our findings demonstrate the effectiveness of BARIS-ERA in advancing underwater instance segmentation, providing a robust and efficient solution.
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