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SAMSnake: A generic contour-based instance segmentation network assisted by Efficient Segment Anything Model
Summary
SAMSNAKE, a new contour-based instance segmentation network, enhances flexibility and precision. It achieves state-of-the-art results on multiple benchmarks using novel contour initialization and optimization modules.
Area of Science:
- Computer Vision
- Deep Learning
- Image Segmentation
Background:
- Contour-based instance segmentation is crucial for precise object boundary detection.
- Existing methods face limitations in flexibility and initialization accuracy.
Purpose of the Study:
- Introduce SAMSnake, a novel contour-based instance segmentation network.
- Enhance the flexibility of contour segmentation for downstream tasks.
- Improve the accuracy of contour initialization and refinement.
Main Methods:
- Decoupled detector from traditional contour segmentation framework.
- Developed EfficientSAM-based Contour Initialization (ECI) module for accurate initial contours.
- Integrated Dynamic Matching Loss (DML) and normalization offsets in the Normalization Contour Optimization (NCO) module.
- Utilized heatmap and boundary map supervision for training stability.
Main Results:
- Achieved state-of-the-art performance across multiple benchmark datasets.
- Reported mAP scores: 36.4% (Cityscapes), 61.4% (SBD), 38.8% (COCO), 36.7% (KINS), 46.0% (COCOA).
- Demonstrated high precision in contour deformation refinement.
Conclusions:
- SAMSNAKE offers a flexible and high-performance solution for contour-based instance segmentation.
- The proposed ECI and NCO modules significantly improve contour initialization and refinement.
- The method sets a new standard in instance segmentation accuracy and efficiency.
