---
title: "PatternCIR Benchmark and TisCIR: Advancing Zero-Shot Composed Image Retrieval in Remote Sensing"
authors:
  - "Zhechun Liang"
  - "Tao Huang"
  - "Fangfang Wu"
  - "Shiwen Xue"
  - "Zhenyu Wang"
  - "Weisheng Dong"
  - "Xin Li"
  - "Guangming Shi"
doi: "10.24963/ijcai.2025/171"
url: "https://ijcai.org/proceedings/2025/171"
pdf: "https://ijcai.org/proceedings/2025/0171.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "1530-1538"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Scene analysis and understanding"
  - "Computer Vision: CV: Image and video retrieval"
  - "Computer Vision: CV: Multimodal learning"
  - "Computer Vision: CV: Vision, language and reasoning"
---

# PatternCIR Benchmark and TisCIR: Advancing Zero-Shot Composed Image Retrieval in Remote Sensing

Zhechun Liang, Tao Huang, Fangfang Wu, Shiwen Xue, Zhenyu Wang, Weisheng Dong, Xin Li, Guangming Shi

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 1530-1538. DOI: [10.24963/ijcai.2025/171](https://doi.org/10.24963/ijcai.2025/171)

## Abstract

Remote sensing composed image retrieval
(RSCIR) is a new vision-language task that takes
a composed query of an image and text, aiming to
search for a target remote sensing image satisfying
two conditions from intricate remote sensing
imagery. However, the existing attribute-based
benchmark Patterncom in RSCIR has significant
flaws, including the lack of query text sentences
and paired triplets, thus making it unable to evaluate the latest methods. To address this, we propose
the Zero-Shot Query Text Generator (ZS-QTG)
that can generate full query text sentences based on
attributes, and then, by capitalizing on ZS-QTG,
we develop the PatternCIR benchmark. PatternCIR rectifies Patterncom’s deficiencies and enables
the evaluation of existing methods. Additionally,
we explore zero-shot composed image retrieval
methods that do not rely on massive pre-collected
triplets for training. Existing methods use only
the text during retrieval, performing poorly in
RSCIR. To improve this, we propose Text-image
Sequential Training of Composed Image Retrieval
(TisCIR). TisCIR undergoes sequential training of
multiple self-masking projection and fine-grained
image attention modules, which endows it with
the capacity to filter out conflicting information
between the image and text, enhancing the retrieval
by utilizing both modalities in harmony. TisCIR
outperforms existing methods by 12.40% to
62.03% on PatternCIR, achieving state-of-the-art
performance in RSCIR. The data and code are
available here.