FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding

• URL: http://arxiv.org/abs/2511.14901v1
• Date: Tue, 18 Nov 2025 20:39:15 GMT
• Title: FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding
• Authors: Zhenshi Li, Weikang Yu, Dilxat Muhtar, Xueliang Zhang, Pengfeng Xiao, Pedram Ghamisi, Xiao Xiang Zhu,
• Abstract summary: Current RS image-text datasets generate global captions from object-level labels.<n>Their direct application to RS data often leads to performance degradation.<n>We propose FarSLIP, a Fine-grained RS LanguageImage Pretraining framework.
• Score: 27.61517650555534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As CLIP's global alignment limits its ability to capture fine-grained details, recent efforts have focused on enhancing its region-text alignment. However, current remote sensing (RS)-specific CLIP variants still inherit this limited spatial awareness. We identify two key limitations behind this: (1) current RS image-text datasets generate global captions from object-level labels, leaving the original object-level supervision underutilized; (2) despite the success of region-text alignment methods in general domain, their direct application to RS data often leads to performance degradation. To address these, we construct the first multi-granularity RS image-text dataset, MGRS-200k, featuring rich object-level textual supervision for RS region-category alignment. We further investigate existing fine-grained CLIP tuning strategies and find that current explicit region-text alignment methods, whether in a direct or indirect way, underperform due to severe degradation of CLIP's semantic coherence. Building on these, we propose FarSLIP, a Fine-grained Aligned RS Language-Image Pretraining framework. Rather than the commonly used patch-to-CLS self-distillation, FarSLIP employs patch-to-patch distillation to align local and global visual cues, which improves feature discriminability while preserving semantic coherence. Additionally, to effectively utilize region-text supervision, it employs simple CLS token-based region-category alignment rather than explicit patch-level alignment, further enhancing spatial awareness. FarSLIP features improved fine-grained vision-language alignment in RS domain and sets a new state of the art not only on RS open-vocabulary semantic segmentation, but also on image-level tasks such as zero-shot classification and image-text retrieval. Our dataset, code, and models are available at https://github.com/NJU-LHRS/FarSLIP.

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