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.

翻译：生物大脑启发了机器学习的多项进展。然而，计算机视觉领域大多数最先进的模型并不像人脑那样运作，这主要是因为它们无法基于更深入的分析来改变或改进其决策/输出。大脑具有循环特性，而这些模型则不具备。因此，探索在现有最先进架构中加入循环机制会产生何种影响，并回答循环机制能否改进现有架构的问题具有重要意义。为此，我们在前馈分割模型的基础上，探索了多种用于图像分割的循环机制类型。我们研究了自组织型、关系型和记忆检索型循环机制，这些机制通过最小化特定能量函数实现。在实验中，我们在人工合成数据与医学影像数据上测试了这些模型，同时分析了高噪声水平与少样本学习场景下的性能表现。实验结果并未验证我们最初的假设——即循环模型在这些场景中应具有更优性能，这表明这些循环架构本身尚不足以超越最先进的前馈版本，该课题仍需进一步深入研究。