确定性模式提案：一种生成采样在模糊分割中的高效替代方法
Deterministic Mode Proposals: An Efficient Alternative to Generative Sampling for Ambiguous Segmentation
AI总结：
本文提出确定性模式提案模型，通过单次前向传递生成固定数量的提案掩码，减少推理时间并提高真实覆盖度，同时无需完整分布信息即可训练。
摘要：
许多分割任务，如医学图像分割或未来状态预测，本质上具有歧义性，意味着多个预测同样正确。当前方法通常依赖生成模型来捕捉这种不确定性。然而，使用这些方法识别分布的潜在模式计算成本高，需要大量样本和事后聚类。本文将焦点从随机采样转向直接生成可能结果。我们引入模式提案模型，一种确定性框架，能够高效地在单次前向传递中生成固定大小的提案掩码集。为处理冗余提案，我们将传统用于目标检测的置信机制适应到分割掩码的高维空间中。我们的方法显著减少了推理时间，同时在真实覆盖度上优于现有生成模型。此外，我们证明该模型可在不完全知晓结果分布的情况下进行训练，适用于实际数据集。最后，我们展示通过分解预训练流模型的速度场，可以高效估计我们提案的先验模式概率。
英文摘要：
Many segmentation tasks, such as medical image segmentation or future state prediction, are inherently ambiguous, meaning that multiple predictions are equally correct. Current methods typically rely on generative models to capture this uncertainty. However, identifying the underlying modes of the distribution with these methods is computationally expensive, requiring large numbers of samples and post-hoc clustering. In this paper, we shift the focus from stochastic sampling to the direct generation of likely outcomes. We introduce mode proposal models, a deterministic framework that efficiently produces a fixed-size set of proposal masks in a single forward pass. To handle superfluous proposals, we adapt a confidence mechanism, traditionally used in object detection, to the high-dimensional space of segmentation masks. Our approach significantly reduces inference time while achieving higher ground-truth coverage than existing generative models. Furthermore, we demonstrate that our model can be trained without knowing the full distribution of outcomes, making it applicable to real-world datasets. Finally, we show that by decomposing the velocity field of a pre-trained flow model, we can efficiently estimate prior mode probabilities for our proposals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
