Summary of Semi-self-supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data For Wheat Head Segmentation, by Alireza Ghanbari et al.
Semi-Self-Supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data for Wheat Head Segmentation
by Alireza Ghanbari, Gholamhassan Shirdel, Farhad Maleki
First submitted to arxiv on: 12 May 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 paper introduces a semi-self-supervised domain adaptation technique for precision agriculture using deep learning. The approach addresses challenges in developing techniques that generalize across different conditions, such as weather and lighting. By leveraging synthesized image-mask pairs and unannotated images, the model achieves effective adaptation to real images. The proposed architecture uses a two-branch convolutional encoder-decoder model that combines both types of data. Experiments demonstrate the model’s potential for developing generalizable solutions, with a Dice score of 80.7% on an internal test dataset and 64.8% on an external test set spanning 18 domains across five countries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computer vision techniques to help farmers make better decisions. Farmers need tools that can work well in different conditions, like sunny days or rainy days. The problem is that creating these tools requires a lot of labeled data, which is time-consuming and expensive. To solve this issue, the researchers developed a new technique that uses some labeled images and many unlabeled images to train a model. They showed that their approach can work well on real-world images from different farms and countries, with an accuracy of 64.8%. This could help farmers make more informed decisions and improve agriculture. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation » Encoder decoder » Mask » Precision » Self supervised
