BoundMatch: Boundary detection applied to semi-supervised segmentation for urban-driving scenes

• URL: http://arxiv.org/abs/2503.23519v1
• Date: Sun, 30 Mar 2025 17:02:26 GMT
• Title: BoundMatch: Boundary detection applied to semi-supervised segmentation for urban-driving scenes
• Authors: Haruya Ishikawa, Yoshimitsu Aoki,
• Abstract summary: Semi-supervised semantic segmentation (SS-SS) aims to mitigate the heavy annotation burden of dense pixel labeling.<n>We propose BoundMatch, a novel multi-task SS-SS framework that integrates semantic boundary detection into the consistency regularization pipeline.<n>Our core mechanism, Boundary Consistency Regularized Multi-Task Learning, enforces prediction agreement between teacher and student models.
• Score: 6.236890292833387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised semantic segmentation (SS-SS) aims to mitigate the heavy annotation burden of dense pixel labeling by leveraging abundant unlabeled images alongside a small labeled set. While current teacher-student consistency regularization methods achieve strong results, they often overlook a critical challenge: the precise delineation of object boundaries. In this paper, we propose BoundMatch, a novel multi-task SS-SS framework that explicitly integrates semantic boundary detection into the consistency regularization pipeline. Our core mechanism, Boundary Consistency Regularized Multi-Task Learning (BCRM), enforces prediction agreement between teacher and student models on both segmentation masks and detailed semantic boundaries. To further enhance performance and sharpen contours, BoundMatch incorporates two lightweight fusion modules: Boundary-Semantic Fusion (BSF) injects learned boundary cues into the segmentation decoder, while Spatial Gradient Fusion (SGF) refines boundary predictions using mask gradients, leading to higher-quality boundary pseudo-labels. This framework is built upon SAMTH, a strong teacher-student baseline featuring a Harmonious Batch Normalization (HBN) update strategy for improved stability. Extensive experiments on diverse datasets including Cityscapes, BDD100K, SYNTHIA, ADE20K, and Pascal VOC show that BoundMatch achieves competitive performance against state-of-the-art methods while significantly improving boundary-specific evaluation metrics. We also demonstrate its effectiveness in realistic large-scale unlabeled data scenarios and on lightweight architectures designed for mobile deployment.

Related papers

• Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID [82.12123628480371]
  Unsupervised person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations for model learning. Previous methods unify pseudo-labels of cross-modality images through label association algorithms and then design contrastive learning framework for global feature learning. We propose a Semantic-Aligned Learning with Collaborative Refinement (SALCR) framework, which builds up objective for specific fine-grained patterns emphasized by each modality.
  arXiv  Detail & Related papers  (2025-04-27T13:58:12Z)
• A Deep Learning Framework for Boundary-Aware Semantic Segmentation [9.680285420002516]
  This study proposes a Mask2Former-based semantic segmentation algorithm incorporating a boundary enhancement feature bridging module (BEFBM)<n>The proposed approach achieves significant improvements in metrics such as mIOU, mDICE, and mRecall.<n>Visual analysis confirms the model's advantages in fine-grained regions.
  arXiv  Detail & Related papers  (2025-03-28T00:00:08Z)
• Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approach [7.012760526318993]
  Weakly-Supervised Semantic (WSSS) offers a cost-efficient workaround to extensive labeling. Existing WSSS methods have difficulties in learning the boundaries of objects leading to poor segmentation results. We propose a novel and effective framework that addresses these issues by leveraging visual foundation models inside the bounding box.
  arXiv  Detail & Related papers  (2024-05-10T16:42:25Z)
• Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding [55.107555305760954]
  We propose a conceptually simple yet effective method that attributes forgetting to layer-wise parameter overwriting and the resulting decision boundary distortion. Our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02x the base model.
  arXiv  Detail & Related papers  (2024-01-17T09:01:29Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation [89.41179071022121]
  Self-training is a prevailing approach in cross-domain semantic segmentation. We propose a novel approach called Semantic Connectivity-driven pseudo-labeling. This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
  arXiv  Detail & Related papers  (2023-12-11T12:29:51Z)
• Boundary Guided Context Aggregation for Semantic Segmentation [23.709865471981313]
  We exploit boundary as a significant guidance for context aggregation to promote the overall semantic understanding of an image. We conduct extensive experiments on the Cityscapes and ADE20K databases, and comparable results are achieved with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-27T17:04:38Z)
• The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation [85.153426159438]
  We propose Basis based Instance(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods. Our B2Inst leads to consistent improvements and accurately parses out the instance boundaries in a scene.
  arXiv  Detail & Related papers  (2020-11-26T11:26:06Z)
• Think about boundary: Fusing multi-level boundary information for landmark heatmap regression [51.48533538153833]
  We study a two-stage but end-to-end approach for exploring the relationship between the facial boundary and landmarks. We get boundary-aware landmark predictions, which consists of two modules: the self-calibrated boundary estimation (SCBE) module and the boundary-aware landmark transform (BALT) module. Our approach outperforms state-of-the-art methods in the literature.
  arXiv  Detail & Related papers  (2020-08-25T10:14:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.