IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme

• URL: http://arxiv.org/abs/2504.09797v1
• Date: Mon, 14 Apr 2025 01:51:29 GMT
• Title: IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme
• Authors: Dinh Dai Quan Tran, Hoang-Thien Nguyen. Thanh-Huy Nguyen, Gia-Van To, Tien-Huy Nguyen, Quan Nguyen,
• Abstract summary: Semi-Supervised Semantic (SSSS) aims to improve segmentation accuracy by leveraging a small set of labeled images alongside a larger pool of unlabeled data.<n>We propose a novel tri-branch semi-supervised segmentation framework incorporating a dual-teacher strategy, named IGL-DT.<n>Our approach employs SwinUnet for high-level semantic guidance through Global Context Learning and ResUnet for detailed feature refinement via Local Regional Learning.
• Score: 3.440487702095727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-Supervised Semantic Segmentation (SSSS) aims to improve segmentation accuracy by leveraging a small set of labeled images alongside a larger pool of unlabeled data. Recent advances primarily focus on pseudo-labeling, consistency regularization, and co-training strategies. However, existing methods struggle to balance global semantic representation with fine-grained local feature extraction. To address this challenge, we propose a novel tri-branch semi-supervised segmentation framework incorporating a dual-teacher strategy, named IGL-DT. Our approach employs SwinUnet for high-level semantic guidance through Global Context Learning and ResUnet for detailed feature refinement via Local Regional Learning. Additionally, a Discrepancy Learning mechanism mitigates over-reliance on a single teacher, promoting adaptive feature learning. Extensive experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches, achieving superior segmentation performance across various data regimes.

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