# A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

arXiv: https://arxiv.org/abs/2008.05225 · HTML: https://mlsanity.com/paper/2008.05225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

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

- [RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework](https://mlsanity.com/paper/2202.05988/) (2022)
- [Zero-shot sketch-based remote sensing image retrieval based on multi-level and attention-guided tokenization](https://mlsanity.com/paper/2402.02141/) (2024)

## Abstract (arXiv)

Conventional existing retrieval methods in remote sensing (RS) are often based on a uni-modal data retrieval framework. In this work, we propose a novel inter-modal triplet-based zero-shot retrieval scheme utilizing a sketch-based representation of RS data. The proposed scheme performs efficiently even when the sketch representations are marginally prototypical of the image. We conducted experiments on a new bi-modal image-sketch dataset called Earth on Canvas (EoC) conceived during this study. We perform a thorough bench-marking of this dataset and demonstrate that the proposed network outperforms other state-of-the-art methods for zero-shot sketch-based retrieval framework in remote sensing.