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<!DOCTYPE html>
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<title>FineGrip</title>
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<h1 class="title is-1 publication-title">Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation</h1>
<div class="is-size-5 publication-authors">
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<span class="author-block">
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Danpei Zhao</a>,</span>
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<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Bo Yuan</a>,
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Ziqiang Chen</a>,
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<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Tian Li</a>,
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<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Zhuoran Liu</a>,
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<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Wentao Li</a>,
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Yue Gao</a>
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<span class="author-block">Beihang University<br>AIRVIC LAB<br>2024</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Current remote-sensing interpretation models often focus on a single task such as detection, segmentation, or caption. However, the task-specific designed models are unattainable to achieve the comprehensive multi-level interpretation of images. The field also lacks support for multi-task joint interpretation datasets. In this paper, we propose Panoptic Perception, a novel task and a new fine-grained dataset (FineGrip) to achieve a more thorough and universal interpretation for RSIs. The new task, 1) integrates pixel-level, instance-level, and image-level information for universal image perception, 2) captures image information from coarse to fine granularity, achieving deeper scene understanding and description, and 3) enables various independent tasks to complement and enhance each other through multi-task learning. By emphasizing multi-task interactions and the consistency of perception results, this task enables the simultaneous processing of fine-grained foreground instance segmentation, background semantic segmentation, and global fine-grained image captioning. Concretely, the FineGrip dataset includes 2,649 remote sensing images, 12,054 fine-grained instance segmentation masks belonging to 20 foreground things categories, 7,599 background semantic masks for 5 stuff classes and 13,245 captioning sentences. Furthermore, we propose a joint optimization-based panoptic perception model. Experimental results on FineGrip demonstrate the feasibility of the panoptic perception task and the beneficial effect of multi-task joint optimization on individual tasks. The dataset will be publicly
available.
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<h2 class="title">NEWs</h2>
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2024.06 - The FineGrip Dataset is publicly available!
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2024.04 - Our paper is accepted in IEEE TGRS.
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<h2 class="subtitle has-text-centered">
Panoptic perception: A comprehensive multi-modal interpretation architecture for remote sensing image.
</h2>
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FineGrip-v1.0: The training and validation sets.
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<h2 class="subtitle has-text-centered">
Panoptic perception covers pixel-level, instance-level and image-level interpretation
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<h2 class="subtitle has-text-centered">
Panoptic perception annotations
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<pre><code>@ARTICLE{PanopticPerception,
author={Zhao, Danpei and Yuan, Bo and Chen, Ziqiang and Li, Tian and Liu, Zhuoran and Li, Wentao and Gao, Yue},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Panoptic Perception: A Novel Task and Fine-Grained Dataset for Universal Remote Sensing Image Interpretation},
year={2024},
volume={62},
number={},
pages={1-14},
doi={10.1109/TGRS.2024.3392778}
}</code></pre>
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