Summary of The Role Of Recurrency in Image Segmentation For Noisy and Limited Sample Settings, by David Calhas et al.
The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings
by David Calhas, João Marques, Arlindo L. Oliveira
First submitted to arxiv on: 20 Dec 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 investigates whether incorporating recurrent mechanisms into existing state-of-the-art computer vision models can improve their performance. The authors draw inspiration from the biological brain, which is capable of adapting and improving its decisions based on analysis. They build upon a feed-forward segmentation model and explore various types of recurrency, including self-organizing, relational, and memory retrieval. The models are tested on artificial and medical imaging data under noisy and few-shot learning conditions. Despite initial expectations, the results show that recurrent architectures do not surpass state-of-the-art feed-forward versions, highlighting the need for further research in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning can be improved by making it more like the human brain. The brain is great at changing its decisions based on new information, and the authors want to see if they can make computer vision models do the same thing. They take a existing model that does well at segmentation and add different types of “memory” or “recurrency” to see if it helps. They test these new models with fake and real medical images, but even when there’s noise or only a little bit of training data, they don’t do much better than the original model. |
Keywords
» Artificial intelligence » Few shot » Machine learning
