A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images
This addresses the need for more flexible retrieval methods in remote sensing, though it appears incremental as it builds on existing zero-shot and sketch-based approaches.
The paper tackles the problem of retrieving remote sensing images using sketch queries in a zero-shot setting, proposing a novel inter-modal triplet-based retrieval scheme that outperforms state-of-the-art methods on a new bi-modal dataset called Earth on Canvas.
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.