The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings
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- TL;DR
- This study investigates whether adding brain-inspired recurrent mechanisms to computer vision models can improve image segmentation in noisy and limited-data settings.
- Problem
- Unlike the human brain, which can refine decisions through deeper analysis, most state-of-the-art computer vision models lack recurrent mechanisms.
- Method
- The researchers built upon a feed-forward segmentation model to test self-organizing, relational, and memory retrieval types of recurrency designed to minimize a specific energy function.
- Results
- Experiments on artificial and medical imaging data under high noise and few-shot settings did not show that recurrent models perform better than standard feed-forward versions.
- Contributions
- An empirical evaluation of multiple recurrent mechanisms integrated into a feed-forward image segmentation architecture.
- Limitations
- Tested recurrent architectures by themselves were found to be insufficient to surpass state-of-the-art feed-forward versions.
- Takeaways
- Adding recurrency alone is not enough to improve existing segmentation architectures in noisy and low-sample settings, indicating that further research is needed.
- Applications
- Image segmentation in medical imaging and artificial data settings.
- Topics
- Image Segmentation, Recurrent Neural Networks, Computer Vision, Few-Shot Learning
- For industry
- Healthcare and medical imaging
- Why it matters
- Provides empirical evidence on the limitations of current recurrent mechanisms in computer vision, guiding future research directions for brain-inspired machine learning.
Abstract
The biological brain has inspired multiple advances in machine learning. However, most state-of-the-art models in computer vision do not operate like the human brain, simply because they are not capable of changing or improving their decisions/outputs based on a deeper analysis. The brain is recurrent, while these models are not. It is therefore relevant to explore what would be the impact of adding recurrent mechanisms to existing state-of-the-art architectures and to answer the question of whether recurrency can improve existing architectures. To this end, we build on a feed-forward segmentation model and explore multiple types of recurrency for image segmentation. We explore self-organizing, relational, and memory retrieval types of recurrency that minimize a specific energy function. In our experiments, we tested these models on artificial and medical imaging data, while analyzing the impact of high levels of noise and few-shot learning settings. Our results do not validate our initial hypothesis that recurrent models should perform better in these settings, suggesting that these recurrent architectures, by themselves, are not sufficient to surpass state-of-the-art feed-forward versions and that additional work needs to be done on the topic.