Summary of Transfer Learning with Generative Models For Object Detection on Limited Datasets, by Matteo Paiano et al.
Transfer learning with generative models for object detection on limited datasets
by Matteo Paiano, Stefano Martina, Carlotta Giannelli, Filippo Caruso
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Numerical Analysis (math.NA)
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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 In this paper, researchers address the challenge of limited data availability in object detection tasks, particularly in fields like marine biology where correctly labeled bounding boxes are crucial. They propose a transfer learning framework that leverages generated images to improve an object detector’s performance in a few-real data regime. The approach uses a diffusion-based generative model pre-trained on large generic datasets, eliminating the need for fine-tuning on the specific domain of interest. This methodology mitigates the labor-intensive task of manual labeling and has significant implications for machine learning applications in various domains. The authors validate their approach using fish detection in an underwater environment and car detection in an urban setting, achieving performance comparable to models trained on thousands of images with only a few hundred input data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists are trying to solve a big problem. They want to help computers learn about objects without needing lots of labeled pictures. This is important because some areas, like studying fish in the ocean, don’t have enough pictures to train computers properly. The team proposes a new way to do this by creating fake images that can help teach computers. This method uses a special kind of AI model that was already trained on many different types of pictures. The scientists tested their approach and found it works well for finding fish in the ocean and cars on roads. This breakthrough could make it easier to train computers for many other tasks. |
Keywords
* Artificial intelligence * Diffusion * Fine tuning * Generative model * Machine learning * Object detection * Transfer learning
