arXiv cs.AI· Ping Guo, Chengzhou Li, Guanchen Meng, Qi Jia, Jinyuan Liu, Zhu Liu, Yu Liu, Zhongxuan Luo, Xin Fan·· 12 小时前AI 评分20
CTFS：面向极少标签前视声呐图像语义分割的协同教师框架
CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited Labels
AI 导读
研究者提出 CTFS 协同教师框架，用于极少标签下的前视声呐图像语义分割，由一个通用教师和多个声呐专用教师组成多教师协同机制，并采用交替引导策略让学生模型兼顾通用语义与声呐特性。
正文 · 原文
Abstract:As one of the most important underwater sensing technologies, forward-looking sonar exhibits unique imaging characteristics. Sonar images are often affected by severe speckle noise, low texture contrast, acoustic shadows, and geometric distortions. These factors make it difficult for traditional teacher-student frameworks to achieve satisfactory performance in sonar semantic segmentation tasks under extremely limited labeled data conditions. To address this issue, we propose a Collaborative Teacher Semantic Segmentation Framework for forward-looking sonar images. This framework introduces a multi-teacher collaborative mechanism composed of one general teacher and multiple sonar-specific teachers. By adopting a multi-teacher alternating guidance strategy, the student model can learn general semantic representations while simultaneously capturing the unique characteristics of sonar images, thereby achieving more comprehensive and robust feature modeling. Considering the challenges of sonar images, which can lead teachers to generate a large number of noisy pseudo-labels, we further design a cross-teacher reliability assessment mechanism. This mechanism dynamically quantifies the reliability of pseudo-labels by evaluating the consistency and stability of predictions across multiple views and multiple teachers, thereby mitigating the negative impact caused by noisy pseudo-labels. Notably, on the FLSMD dataset, when only 2% of the data is labeled, our method achieves a 5.08% improvement in mIoU compared to other state-of-the-art approaches.
| Comments: | Accepted to CVPR 2026 Finding. Code: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.21071 [cs.CV] |
| (or arXiv:2603.21071v4 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.21071 arXiv-issued DOI via DataCite |
Submission history
From: Ping Guo [view email]
[v1]
Sun, 22 Mar 2026 05:57:36 UTC (2,811 KB)
[v2]
Sat, 16 May 2026 13:16:55 UTC (2,811 KB)
[v3]
Mon, 19 Oct 2026 16:53:21 UTC (2,811 KB)
[v4]
Thu, 5 Nov 2026 16:30:56 UTC (2,811 KB)
来源：arXiv cs.AI · arxiv.org