ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model

• URL: http://arxiv.org/abs/2406.10855v1
• Date: Sun, 16 Jun 2024 09:02:01 GMT
• Title: ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model
• Authors: Song Zhang, Qingzhong Wang, Junyi Liu, Haoyi Xiong,
• Abstract summary: We introduce an innovative auto-labeling framework named ALPS (Automatic Labeling for Pre-training in Pre-training in Remote Sensing) We leverage the Segment Anything Model (SAM) to predict precise pseudo-labels for RS images without necessitating prior annotations or additional prompts. Our approach enhances the performance of downstream tasks across various benchmarks, including iSAID and ISPRS Potsdam.
• Score: 32.91528641298171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the fast-growing field of Remote Sensing (RS) image analysis, the gap between massive unlabeled datasets and the ability to fully utilize these datasets for advanced RS analytics presents a significant challenge. To fill the gap, our work introduces an innovative auto-labeling framework named ALPS (Automatic Labeling for Pre-training in Segmentation), leveraging the Segment Anything Model (SAM) to predict precise pseudo-labels for RS images without necessitating prior annotations or additional prompts. The proposed pipeline significantly reduces the labor and resource demands traditionally associated with annotating RS datasets. By constructing two comprehensive pseudo-labeled RS datasets via ALPS for pre-training purposes, our approach enhances the performance of downstream tasks across various benchmarks, including iSAID and ISPRS Potsdam. Experiments demonstrate the effectiveness of our framework, showcasing its ability to generalize well across multiple tasks even under the scarcity of extensively annotated datasets, offering a scalable solution to automatic segmentation and annotation challenges in the field. In addition, the proposed a pipeline is flexible and can be applied to medical image segmentation, remarkably boosting the performance. Note that ALPS utilizes pre-trained SAM to semi-automatically annotate RS images without additional manual annotations. Though every component in the pipeline has bee well explored, integrating clustering algorithms with SAM and novel pseudo-label alignment significantly enhances RS segmentation, as an off-the-shelf tool for pre-training data preparation. Our source code is available at: https://github.com/StriveZs/ALPS.

Related papers

• Adaptively Augmented Consistency Learning: A Semi-supervised Segmentation Framework for Remote Sensing [9.867670296534103]
  AACL is a semi-supervised segmentation framework designed to enhance RS segmentation accuracy under limited labeled data. AACL achieves competitive performance in semi-supervised segmentation, showing up to a 20% improvement in specific categories and 2% increase in overall performance compared to state-of-the-art frameworks.
  arXiv  Detail & Related papers  (2024-11-14T10:47:01Z)
• SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis [8.489318619991534]
  Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rsfMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision.
  arXiv  Detail & Related papers  (2024-09-24T18:35:57Z)
• Adapting Segment Anything Model for Unseen Object Instance Segmentation [70.60171342436092]
  Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder.
  arXiv  Detail & Related papers  (2024-09-23T19:05:50Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model [85.85899655118087]
  We develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude.
  arXiv  Detail & Related papers  (2023-05-03T10:58:07Z)
• SEPT: Towards Scalable and Efficient Visual Pre-Training [11.345844145289524]
  Self-supervised pre-training has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. We build a task-specific self-supervised pre-training framework based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains.
  arXiv  Detail & Related papers  (2022-12-11T11:02:11Z)
• MSeg: A Composite Dataset for Multi-domain Semantic Segmentation [100.17755160696939]
  We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. We reconcile the generalization and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
  arXiv  Detail & Related papers  (2021-12-27T16:16:35Z)
• 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)
• RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training [77.62171090230986]
  We propose an easily scalable and self-supervised technique that can be used to pre-train any semantic RGB segmentation method. In particular, our pre-training approach makes use of automatically generated labels that can be obtained using depth sensors. We show how our proposed self-supervised pre-training with HN-labels can be used to replace ImageNet pre-training.
  arXiv  Detail & Related papers  (2020-02-06T11:16:24Z)

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.