Deterministic Mode Proposals: An Efficient Alternative to Generative Sampling for Ambiguous Segmentation

2026-03-20

AI Summary

This work addresses the challenge of uncertainty modeling in ambiguous segmentation tasks, where solutions exhibit multimodal distributions. Existing generative approaches rely heavily on extensive sampling and post-processing, incurring high computational costs. To overcome this, the authors propose a deterministic multi-solution generation framework that directly outputs a fixed number of high-quality segmentation masks in a single forward pass, augmented with a confidence mechanism to eliminate redundant predictions. The method abandons stochastic sampling and, for the first time, enables efficient deterministic modeling of the multimodal solution space without requiring knowledge of the full output distribution during training. By leveraging a pretrained flow model to decompose velocity fields and estimate mode prior probabilities, the approach significantly reduces inference time while achieving superior ground-truth coverage compared to current generative models on real-world datasets.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• ambiguous segmentation
• mode identification
• generative sampling
• uncertainty modeling
• segmentation masks

Innovation

• deterministic mode proposals
• ambiguous segmentation
• generative sampling
• confidence mechanism
• mode probability estimation