SparseUWSeg: Active Sparse Point-Label Augmentation for Underwater Semantic Segmentation

• URL: http://arxiv.org/abs/2510.10163v1
• Date: Sat, 11 Oct 2025 10:56:48 GMT
• Title: SparseUWSeg: Active Sparse Point-Label Augmentation for Underwater Semantic Segmentation
• Authors: César Borja, Carlos Plou, Rubén Martinez-Cantín, Ana C. Murillo,
• Abstract summary: We present SparseUWSeg, a novel framework for semantic segmentation.<n>SparseUWSeg employs an active sampling strategy to guide annotators, maximizing the value of their point labels.<n> Experiments on two diverse underwater datasets demonstrate the benefits of SparseUWSeg over state-of-the-art approaches.
• Score: 5.595626117136082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic segmentation is essential to automate underwater imagery analysis with ecology monitoring purposes. Unfortunately, fine grained underwater scene analysis is still an open problem even for top performing segmentation models. The high cost of obtaining dense, expert-annotated, segmentation labels hinders the supervision of models in this domain. While sparse point-labels are easier to obtain, they introduce challenges regarding which points to annotate and how to propagate the sparse information. We present SparseUWSeg, a novel framework that addresses both issues. SparseUWSeg employs an active sampling strategy to guide annotators, maximizing the value of their point labels. Then, it propagates these sparse labels with a hybrid approach leverages both the best of SAM2 and superpixel-based methods. Experiments on two diverse underwater datasets demonstrate the benefits of SparseUWSeg over state-of-the-art approaches, achieving up to +5\% mIoU over D+NN. Our main contribution is the design and release of a simple but effective interactive annotation tool, integrating our algorithms. It enables ecology researchers to leverage foundation models and computer vision to efficiently generate high-quality segmentation masks to process their data.

Related papers

• Bridge the Points: Graph-based Few-shot Segment Anything Semantically [79.1519244940518]
  Recent advancements in pre-training techniques have enhanced the capabilities of vision foundation models. Recent studies extend the SAM to Few-shot Semantic segmentation (FSS) We propose a simple yet effective approach based on graph analysis.
  arXiv  Detail & Related papers  (2024-10-09T15:02:28Z)
• Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation [79.05949524349005]
  We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from saliency maps. We also propose a cross-task affinity learning mechanism to learn pixel-level affinities from the saliency and segmentation feature maps.
  arXiv  Detail & Related papers  (2024-03-02T10:03:21Z)
• A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing [10.497309421830671]
  Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully supervised manner. This work presents a general and simple framework to tackle point clouds understanding when labels are limited.
  arXiv  Detail & Related papers  (2023-12-03T02:38:51Z)
• A Lightweight Clustering Framework for Unsupervised Semantic Segmentation [28.907274978550493]
  Unsupervised semantic segmentation aims to categorize each pixel in an image into a corresponding class without the use of annotated data. We propose a lightweight clustering framework for unsupervised semantic segmentation. Our framework achieves state-of-the-art results on PASCAL VOC and MS COCO datasets.
  arXiv  Detail & Related papers  (2023-11-30T15:33:42Z)
• Deep Multi-View Subspace Clustering with Anchor Graph [11.291831842959926]
  We propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG) DMCAG learns the embedded features for each view independently, which are used to obtain the subspace representations. Our method achieves superior clustering performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-11T16:17:43Z)
• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision [63.429704654271475]
  We propose a novel weakly supervised method RWSeg that only requires labeling one object with one point. With these sparse weak labels, we introduce a unified framework with two branches to propagate semantic and instance information. Specifically, we propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages competition among different instance graphs.
  arXiv  Detail & Related papers  (2022-08-10T02:14:39Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation [90.87105131054419]
  We present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains.
  arXiv  Detail & Related papers  (2020-12-19T21:18:03Z)

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.