Summary of D4: Text-guided Diffusion Model-based Domain Adaptive Data Augmentation For Vineyard Shoot Detection, by Kentaro Hirahara et al.
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection
by Kentaro Hirahara, Chikahito Nakane, Hajime Ebisawa, Tsuyoshi Kuroda, Yohei Iwaki, Tomoyoshi Utsumi, Yuichiro Nomura, Makoto Koike, Hiroshi Mineno
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel generative data augmentation method, called D4, for plant phenotyping using object detection models in agricultural fields. The challenge lies in collecting high-precision training data due to annotation difficulties and domain diversity. To overcome this, D4 uses a pre-trained text-guided diffusion model based on original images from video data and annotated datasets. This approach generates new annotated images with adapted background information, overcoming the lack of training data in agriculture. The method improves mean average precision by up to 28.65% for BBox detection and average precision by up to 13.73% for keypoint detection in vineyard shoot detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to create better pictures of plants using machine learning models. This is hard because it’s hard to label the data needed to train the models, and different types of crops have different characteristics. The researchers created a new method that uses existing images and some labeled data to generate more labeled images that are similar to the ones they need. This helps improve the accuracy of plant detection models. |
Keywords
» Artificial intelligence » Data augmentation » Diffusion model » Machine learning » Mean average precision » Object detection » Precision
