# Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation

arXiv: https://arxiv.org/abs/2606.21863 · HTML: https://mlsanity.com/paper/2606.21863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference](https://mlsanity.com/paper/2312.01597/) (2023)
- [ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference](https://mlsanity.com/paper/2407.12442/) (2024)
- [ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2408.04883/) (2024)
- [CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2411.10086/) (2024)
- [SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images](https://mlsanity.com/paper/2410.01768/) (2024)
- [PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images](https://mlsanity.com/paper/2409.13401/) (2024)
- [SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2512.08730/) (2025)
- [Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images](https://mlsanity.com/paper/2508.18067/) (2025)
- [SOTA: Self-adaptive Optimal Transport for Zero-Shot Classification with Multiple Foundation Models](https://mlsanity.com/paper/2506.13723/) (2025)
- [Taming SAM3 in the Wild: A Concept Bank for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2602.06333/) (2026)
- [ConInfer: Context-Aware Inference for Training-Free Open-Vocabulary Remote Sensing Segmentation](https://mlsanity.com/paper/2603.29271/) (2026)
- [VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference](https://mlsanity.com/paper/2605.12325/) (2026)

## Built on by (most cited first, 2 of 2)

- [A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2610.04300/) (2026)
- [TAG: Calibrating SAM3 for Universal Image Segmentation without Dense Supervision](https://mlsanity.com/paper/2610.14651/) (2026)

## Abstract (arXiv)

Open-vocabulary semantic segmentation (OVSS) in remote sensing images aims to segment categories beyond a fixed label space. Recent SAM 3-based methods provide a promising training-free foundation, yet three key issues remain: (1) a single class-name prompt lacks sufficient semantic coverage for complex remote sensing categories; (2) expanding each category into multiple prompts introduces redundant online text encoding; and (3) directly aggregating multiple prompt responses propagates noisy activations into the final prediction. To address these issues, we propose ProC-SAM3, which calibrates SAM 3's prompt interface for remote sensing OVSS from three complementary aspects. First, we construct an offline prompt pool where a Category Matcher groups MLLM-generated candidates into per-category sets, and Expansion Constraints further refine each set using category-specific prior knowledge. Second, the resulting text embeddings are cached and reused across all test images, eliminating repeated text encoding. Third, we introduce Presence-Guided Residual Fusion to gate unreliable decoder outputs by prompt presence and confidence, followed by peak-preserving class aggregation that retains fine-grained activations for small and sparse objects. Experiments on eight benchmarks show that ProC-SAM3 achieves an average mIoU of 56.1%, outperforming the previous best training-free method by 3.9 percentage points. Code will be available at https://github.com/YanghuiSong/ProC-SAM3.