Deterministic Mode Proposals: An Efficient Alternative to Generative Sampling for Ambiguous Segmentation
| Authors | Sebastian Gerard & Josephine Sullivan |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2603.20191 |
| Download | |
| Categories | cs.CV |
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.
Engineering Breakdown
Plain English
This paper tackles the problem of ambiguous segmentation tasks where multiple correct outputs exist, like medical image segmentation or predicting future states. Instead of using expensive generative models that require many samples and post-hoc clustering to find modes, the authors propose mode proposal models—a deterministic approach that produces a fixed set of proposal masks in a single forward pass. They adapt a confidence mechanism from object detection to filter out unnecessary proposals, making the method computationally efficient while still capturing the underlying distribution modes.
Core Technical Contribution
The core innovation is shifting from stochastic sampling-based uncertainty quantification to deterministic mode proposal generation. Rather than sampling hundreds or thousands of masks and clustering them offline, mode proposal models directly output a fixed-size set of likely candidate masks in one forward pass. This is fundamentally different from prior generative approaches because it treats multimodal prediction as a structured proposal problem, borrowing confidence mechanisms from object detection to handle the variable importance of proposals. The deterministic nature eliminates the computational overhead of sampling while maintaining the ability to capture multiple valid solutions.
How It Works
The system takes an input image and runs a single forward pass through a deterministic neural network to generate a fixed number of proposal masks (e.g., 5 or 10 masks per image). Each proposal has an associated confidence score indicating how likely it represents a true mode of the solution distribution. The network is trained to learn which multiple solutions are plausible for ambiguous cases, similar to how object detectors learn to output multiple bounding boxes with confidence scores. A confidence mechanism—adapted from detection frameworks—filters or ranks these proposals, keeping high-confidence ones and discarding redundant or unlikely masks. The output is a compact set of discrete predictions representing the major modes rather than a continuous stochastic distribution.
Production Impact
For medical imaging teams, this approach dramatically reduces inference latency and memory requirements compared to sampling-based methods—one forward pass instead of 50-100 samples plus clustering overhead. Production pipelines can now efficiently handle multimodal predictions at scale without expensive GPU hours for sampling and post-processing. The confidence scores provide built-in interpretability, allowing clinicians or downstream systems to understand which proposals are model-confident versus speculative. Trade-offs include: you must know the expected number of modes in advance (fixed output size), the method may miss rare or unusual modes not seen during training, and it requires careful labeling of multiple ground truths during training. Integration is straightforward since it replaces existing segmentation heads without fundamental pipeline restructuring.
Limitations and When Not to Use This
The method assumes the number of plausible modes is known and fixed in advance, which may not hold for truly open-ended problems where the number of valid solutions varies. It may struggle with rare modes or long-tail scenarios not well-represented in training data, since the deterministic approach won't naturally explore the full solution space like sampling methods do. The approach depends on having high-quality multi-annotated datasets during training to learn what constitutes different valid modes, which is expensive to obtain. The paper doesn't fully address how to set the proposal count or confidence thresholds across different task domains, leaving practitioners with manual tuning challenges.
Research Context
This work builds on decades of research in handling ambiguity in computer vision, extending beyond simple maximum-a-posteriori estimation to explicit multimodal prediction. It connects to recent progress in generative models for vision (diffusion, flow models) while rejecting their computational expense in favor of deterministic efficiency. The confidence mechanism borrows directly from object detection literature (YOLO, Faster R-CNN), showing cross-domain transfer of detection techniques to dense prediction. The paper likely advances benchmarks in medical image segmentation and video prediction tasks where multiple plausible futures or anatomical interpretations are known to exist.
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