探究循环机制能否提升噪声与小样本下的图像分割性能
The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings
- 在前馈模型中引入自组织、关系与记忆检索三种循环结构
- 在高噪声和少样本场景下，循环模型未超越前馈基线
- 结果表明单纯增加循环性不足以提升性能，需进一步研究
生物大脑启发了机器学习的多项进展，但当前计算机视觉中的主流模型并不像人脑那样能基于深度分析动态调整决策。人脑具有循环特性，而现有模型则不具备。因此有必要探索在先进架构中加入循环机制的影响，并验证其是否能提升性能。为此，我们在前馈分割模型基础上，尝试了多种循环结构——包括自组织、关系建模和记忆检索，这些结构均通过最小化特定能量函数实现。实验在人工数据和医学影像上进行，评估了高噪声与少样本设置下的表现。结果并未支持初始假设：循环模型在这些场景下表现更优。这表明，仅靠循环结构本身不足以超越现有的前馈模型，该领域仍需深入探索。
原文摘要 · Abstract (English)
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.
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