Summary of Adatreeformer: Few Shot Domain Adaptation For Tree Counting From a Single High-resolution Image, by Hamed Amini Amirkolaee et al.
AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a Single High-Resolution Image
by Hamed Amini Amirkolaee, Miaojing Shi, Lianghua He, Mark Mulligan
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel framework for estimating tree density using aerial or satellite images, which is crucial for forest management. The challenge lies in the diverse range of trees and topography, making it difficult for existing models to perform well. AdaTreeFormer, the proposed method, combines a shared encoder with hierarchical feature extraction and three subnets: two for self-domain attention maps and one for cross-domain attention maps. An attention-to-adapt mechanism and hierarchical cross-domain feature alignment scheme are introduced to reduce the gap between source and target domains. Adversarial learning is also incorporated to further improve the model’s performance. The paper evaluates AdaTreeFormer on six domain adaptation tasks using three tree counting datasets, achieving a significant reduction in absolute counting errors and increase in accuracy of detected trees’ locations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to count trees in pictures from space. Right now, it’s hard to do this accurately because there are many different types of trees and the landscape is varied. The authors created a new way called AdaTreeFormer that uses a combination of algorithms to make tree counting more accurate. They tested this method on three different datasets and found that it worked much better than previous methods. |
Keywords
* Artificial intelligence * Alignment * Attention * Domain adaptation * Encoder * Feature extraction
