Summary of Panoptic Perception: a Novel Task and Fine-grained Dataset For Universal Remote Sensing Image Interpretation, by Danpei Zhao et al.
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation
by Danpei Zhao, Bo Yuan, Ziqiang Chen, Tian Li, Zhuoran Liu, Wentao Li, Yue Gao
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Panoptic Perception task integrates pixel-level, instance-level, and image-level information for universal image perception, capturing coarse to fine granularity and achieving deeper scene understanding and description. This novel task enables simultaneous processing of fine-grained foreground instance segmentation, background semantic segmentation, and global fine-grained image captioning through multi-task learning. The paper proposes a FineGrip dataset containing 2,649 remote sensing images, 12,054 instance segmentation masks, 7,599 background semantic masks, and 13,245 captioning sentences. A joint optimization-based panoptic perception model is also proposed, demonstrating the feasibility of the task and the beneficial effect of multi-task joint optimization on individual tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Panoptic Perception is a new way to understand images by looking at them from different levels – pixel, instance, and image. This helps us get a deeper understanding of what’s happening in an image. The goal is to be able to do multiple things with one image, like separate objects, describe the background, and tell a story about what’s happening. To make this happen, we created a big dataset called FineGrip that has lots of images, masks, and sentences. We also came up with a special way to train a model to do all these tasks at once. |
Keywords
» Artificial intelligence » Image captioning » Instance segmentation » Multi task » Optimization » Scene understanding » Semantic segmentation
