arXiv AI

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing

The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.

Hugging Face Trending Papers

Aug 11

GeoSeg-OV: Bridging Geospatial Gaps with Structural Guidance for Open-Vocabulary Remote Sensing Segmentation

Open-vocabulary remote sensing segmentation has recently emerged as a promising paradigm that enables pixel-level recognition of arbitrary categories specified by natural language, including classes unseen during training. However, geospatial domain shifts caused by heterogeneous regions, spatial resolutions, and acquisition platforms weaken visual-text matching and limit cross-dataset generalization.

rag computer-vision benchmarks

arXiv AI

Aug 5

Standalone DINOv3 for Training-Free Open-Vocabulary Semantic Segmentation in Remote Sensing

arXiv:2608. 03023v1 Announce Type: cross Abstract: Remote sensing semantic segmentation is hindered by costly pixel-level annotations, motivating training-free open-vocabulary methods.

By Changhao Zhao, Haoxiang Li, Yuke Li, Hai Liu, LingLin Zeng

computer-vision fine-tuning multimodal

arXiv Computer Vision

Sep 22

SatOV: Restoring Spatial Priors for Training-Free Open-Vocabulary Segmentation in Remote Sensing Imagery

arXiv:2609.22834v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVS) of remote sensing imagery is a challenging pixel-level task requiring strong generalization and adaptation...

By Changhao Zhao, Linglin Zeng, Hai Liu

computer-vision multimodal benchmarks

arXiv Computer Vision

Sep 16

VPRef: A Cross-Domain Benchmark for Referring Remote Sensing Image Segmentation

The paper introduces VPRef, the first cross‑domain benchmark for Referring Remote Sensing Image Segmentation, containing 46,972 language‑image‑annotation triplets with a three‑tier linguistic hierarchy. It proposes a parameter‑efficient adaptation method based on the Segment Anything Model and Low‑Rank Adaptation, using pseudo‑label self‑training for visual drift and random multi‑granularity prompt mixing for textual drift. Experiments show the approach improves cross‑domain segmentation while altering only 1.08 % of the base model’s parameters, offering a strong baseline for future research.

By Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang

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Hugging Face Trending Papers

Sep 15

VPRef: A Cross-Domain Benchmark for Referring Remote Sensing Image Segmentation

Rapid advancements in vision-language models have propelled Referring Remote Sensing Image Segmentation (RRSIS) to the forefront of Earth observation. However, practical deployments suffer severe perf...

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arXiv Computer Vision

Sep 25

OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding

OptiSAR-Net++ introduces a new cross‑domain remote sensing visual grounding task (CD‑RSVG) and the first large‑scale benchmark dataset, OptSAR‑RSVG. The framework replaces Transformer decoding with a CLIP‑based contrastive approach, employing a patch‑level Low‑Rank Adaptation Mixture of Experts for efficient cross‑domain feature decoupling and a text‑guided dual‑gate fusion module for improved semantic‑visual alignment. Experiments show state‑of‑the‑art performance on OptSAR‑RSVG and DIOR‑RSVG, with notable gains in localization accuracy and computational efficiency.

By Xiaoyu Tang, Jun Dong, Jintao Cheng, Rui Fan

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arXiv AI

4d ago

ActiveSAM: Fast and Accurate Open-Vocabulary Semantic Segmentation with Frozen SAM 3

arXiv:2606.16996v2 Announce Type: replace-cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabu...

By Tran Dinh Tien, Zhiqiang Shen

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arXiv Computer Vision

Sep 16

From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation

arXiv:2505.15147v3 Announce Type: replace Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the...

By Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang

computer-vision multimodal

arXiv AI

Sep 4

Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Data

The paper introduces OmniRSCLIP, an end‑to‑end contrastive learning framework that extends the CLIP architecture to handle heterogeneous remote sensing sensors such as SAR, multi‑spectral imaging, and hyperspectral imaging. It achieves this by employing Spectral‑Spatial Basis Decomposition to adapt arbitrary‑channel inputs without losing pretrained visual knowledge, and a spectral‑context‑aware mask‑based contrastive learning scheme to improve fine‑grained image‑text alignment. The authors also build OmniRS5M, a large‑scale image‑text corpus covering multiple sensor modalities, and demonstrate that OmniRSCLIP maintains strong RGB performance while effectively supporting these diverse remote sensing data types.

By Xiangyang Miao, Kelu Yao, Yekai Huang, Xiaogang Xu, Junxiao Xue, Minjun Shen, Chenghui Lv, Shanji Liu, Yaying Chen, Chao Li

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arXiv AI

Jun 30

RSGPNet: Geometric Prompting for Remote Sensing Open-Vocabulary Semantic Segmentation

arXiv:2606. 28410v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-world understanding.

By Shanwen Wang, Xin Sun, Sirui Wang, Xiao Xiang Zhu

llms computer-vision benchmarks safety

arXiv AI

Sep 2

SARTM: Segment Any RGB Thermal Model with Language aided Distillation

The paper introduces SARTM, a framework that adapts the Segment Anything Model (SAM) for RGB‑thermal (RGB‑T) semantic segmentation. It fine‑tunes SAM with LoRA layers, incorporates language guidance, and employs a Cross‑Modal Knowledge Distillation module to bridge modality gaps. The approach also modifies the segmentation head and adds an auxiliary semantic head, achieving superior performance on MFNET, PST900, and FMB benchmarks.

By Dong Xing, Jinhe Zhang, Hang Yang, Yuqing Wang

computer-vision fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 20

More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe

arXiv:2607. 15942v1 Announce Type: cross Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.

By Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")

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