Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Song, Wang, Qingzhong, Liu, Junyi, Xiong, Haoyi
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Semantic Localization Guiding Segment Anything Model For Reference Remote Sensing Image Segmentation
by: Li, Shuyang, et al.
Published: (2025)

DiffRIS: Enhancing Referring Remote Sensing Image Segmentation with Pre-trained Text-to-Image Diffusion Models
by: Dong, Zhe, et al.
Published: (2025)

RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
by: Li, Yonglin, et al.
Published: (2023)

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
by: Tseng, Gabriel, et al.
Published: (2023)

Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
by: Liu, Xianjie, et al.
Published: (2023)

Compress Any Segment Anything Model (SAM)
by: Fan, Juntong, et al.
Published: (2025)

SAMAug: Point Prompt Augmentation for Segment Anything Model
by: Dai, Haixing, et al.
Published: (2023)

X-SAM: From Segment Anything to Any Segmentation
by: Wang, Hao, et al.
Published: (2025)

Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)
by: Chen, Hongruixuan, et al.
Published: (2024)

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing
by: Li, Bingyu, et al.
Published: (2025)

Graph Information Bottleneck for Remote Sensing Segmentation
by: Shou, Yuntao, et al.
Published: (2023)

Segment Anything for Videos: A Systematic Survey
by: Zhang, Chunhui, et al.
Published: (2024)

Segment and Matte Anything in a Unified Model
by: Fan, Zezhong, et al.
Published: (2026)

Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation
by: Dong, Zhe, et al.
Published: (2024)

Improving Pre-trained Segmentation Models using Post-Processing
by: Parida, Abhijeet, et al.
Published: (2025)

Remote SAMsing: From Segment Anything to Segment Everything
by: de Carvalho, Osmar Luiz Ferreira, et al.
Published: (2026)

SAM 3: Segment Anything with Concepts
by: Carion, Nicolas, et al.
Published: (2025)

Open-Vocabulary Remote Sensing Image Semantic Segmentation
by: Cao, Qinglong, et al.
Published: (2024)

LENS: Learning to Segment Anything with Unified Reinforced Reasoning
by: Zhu, Lianghui, et al.
Published: (2025)

Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images
by: Liu, Qingyuan, et al.
Published: (2024)

Tracking and Segmenting Anything in Any Modality
by: Zhang, Tianlu, et al.
Published: (2025)

Segment Anything in Pathology Images with Natural Language
by: Chen, Zhixuan, et al.
Published: (2025)

Vision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Images
by: Wang, Shanwen, et al.
Published: (2026)

MPerS: Dynamic MLLM MixExperts Perception-Guided Remote Sensing Scene Segmentation
by: Wang, Ziyi, et al.
Published: (2026)

Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
by: Yan, Jun, et al.
Published: (2024)

Referring Remote Sensing Image Segmentation with Cross-view Semantics Interaction Network
by: Yang, Jiaxing, et al.
Published: (2025)

Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts
by: Zhu, Chenyang, et al.
Published: (2024)

CroBIM-U: Uncertainty-Driven Referring Remote Sensing Image Segmentation
by: Sun, Yuzhe, et al.
Published: (2026)

SparseSAM: Structured Sparsification of Activations in Segment Anything Models
by: Tran, Hoai-Chau, et al.
Published: (2026)

EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss
by: Zhang, Zhuoyang, et al.
Published: (2024)

Red-Teaming Segment Anything Model
by: Jankowski, Krzysztof, et al.
Published: (2024)

Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
by: Wang, Guanchun, et al.
Published: (2026)

GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery
by: Jiang, Lifan, et al.
Published: (2026)

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation
by: Tang, Zixuan, et al.
Published: (2026)

Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment
by: Lu, Haisheng, et al.
Published: (2024)

Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain
by: Immanuel, Steve Andreas, et al.
Published: (2024)

TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
by: Wang, Tianyang, et al.
Published: (2025)

WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
by: Zhu, Lianghui, et al.
Published: (2024)

Region-Guided Attack on the Segment Anything Model (SAM)
by: Liu, Xiaoliang, et al.
Published: (2024)

Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream Assessment
by: Xiu, Haoyi, et al.
Published: (2025)