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| Hauptseite > Workflowsammlungen > In Bearbeitung > Contour Proposal Networks with Deep Refinement for Dense High-Throughput Instance Segmentation |
| Hauptseite > Workflowsammlungen > In Bearbeitung > Contour Proposal Networks with Deep Refinement for Dense High-Throughput Instance Segmentation |
| Conference Presentation (After Call) | FZJ-2026-03287 |
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2026
Abstract: Instance segmentation in crowded scenes requires both high object capacity and precise boundary modeling. Recent promptable foundation models offer strong generalization, but their automatic mask generation can be unreliable outside the training domain and their computational cost scales unfavorably with very large numbers of instances. We present CPNv2, a modernized contour-based instance segmentation framework that combines dense proposal prediction with learned, sub-pixel accurate refinement. CPNv2 introduces centroid-based Hungarian target assignment to reduce ambiguous supervision on low-resolution output grids, iterative score refinement for stable confidence estimation, differentiable feature sampling via bilinear interpolation for continuous contour updates, and deep contour refinement with a contour refinement transformer trained with contour denoising. Across our experiments, CPNv2 consistently improves over the previously established CPN architecture and achieves strong results across diverse scientific imaging datasets in terms of F1@50 and PQ@50 scores. At the same time, it preserves a key strength of contour-based segmentation: among the evaluated methods in our experiments, contour-based models show the highest efficiency in terms of latency, memory, and output size. This makes CPNv2 particularly attractive for high-throughput settings, where large images with many instances must be processed efficiently without sacrificing segmentation quality. When benchmarking recent SAM variants we find that even under ground-truth prompting, they do not consistently match the strongest task-specific models on our datasets. Code and trained models will be made publicly available.
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