# Image-Conditioned Instance Prompt Network for Referring Remote Sensing Image Segmentation

arXiv: https://arxiv.org/abs/2605.24532 · HTML: https://mlsanity.com/paper/2605.24532/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 23 May 2026.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2312.12470/) (2023)
- [RRSIS: Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2306.08625/) (2023)
- [RemoteSAM: Towards Segment Anything for Earth Observation](https://mlsanity.com/paper/2505.18022/) (2025)
- [Visual Instance-aware Prompt Tuning](https://mlsanity.com/paper/2507.07796/) (2025)
- [Exploring Fine-Grained Image-Text Alignment for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2409.13637/) (2024)
- [PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation](https://mlsanity.com/paper/2401.13051/) (2024)
- [Referring Remote Sensing Image Segmentation via Bidirectional Alignment Guided Joint Prediction](https://mlsanity.com/paper/2502.08486/) (2025)
- [Multimodal-Aware Fusion Network for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2503.11183/) (2025)
- [SACANet: scene-aware class attention network for semantic segmentation of remote sensing images](https://mlsanity.com/paper/2304.11424/) (2023)

## Abstract (arXiv)

Referring Remote Sensing Image Segmentation (RRSIS) is a situated, task-driven cross-modal task related to the embodied perception paradigm, requiring models to align visual-spatial features with linguistic intentions for precise target perception. Recent research has focused on refining the granularity of textual features and optimizing image-text feature fusion to better guide target feature representations. However, insufficient descriptive granularity and sensitivity to semantic shifts can cause bottlenecks in cross-modal feature fusion. To address these issues, we propose the Image-Conditioned Instance Prompt Network (ICIPNet) with Bilateral Information Fusion, which is designed to alleviate bottlenecks in cross-modal feature fusion. ICIPNet introduces an Image-Conditioned Instance Prompt (ICIP) module to generate self-adaptive visual and semantic representations without external knowledge. The Bilateral Information Fusion (BIF) module enhances feature fusion along the token and channel dimensions. Experiments demonstrate that the proposed ICIPNet outperforms existing RRSIS models.