- The paper introduces a novel single-shot framework that fuses classical level set evolution with deep learning for box-supervised instance segmentation.
- It iteratively refines segmentation boundaries using a differentiable energy function and enhanced mask prediction, outperforming prior box-supervised methods.
- The approach significantly reduces annotation costs while maintaining competitive accuracy, paving the way for more efficient segmentation research.
Box-supervised Instance Segmentation with Level Set Evolution
The paper presents a novel approach to the burgeoning field of box-supervised instance segmentation by integrating the classical level set model with deep learning. Traditional approaches in instance segmentation rely on fully-supervised methods that require pixel-wise mask annotations, leading to significant labeling costs. In contrast, this method leverages simpler box annotations to facilitate segmentation, which has emerged as an attractive alternative for research exploration.
The authors propose a single-shot framework that synergizes the Chan-Vese energy-based level set model with neural networks. This integration is achieved by iteratively learning a series of level set functions that enable implicit curve evolution within an annotated bounding box. The iterative learning process uses a differentiable energy function as the optimization target, which is uniquely adapted for end-to-end training.
Key to this architecture is the adaptation of a mask prediction model, SOLOv2, which generates instance-aware mask maps treated as level sets for each instance. These mask maps, combined with both the input image and its deep features, serve as input data for the level set evolution process. The authors employ a box projection function to derive an initial boundary, which is then refined through iterative optimization by minimizing the defined energy function within each bounding box. This technique effectively bridged box annotation with pixel-level instance distinctions.
The experimental results on multiple datasets including COCO, Pascal VOC, iSAID, and LiTS, suggest that this approach exhibits state-of-the-art performance among box-supervised methods, particularly outperforming existing techniques like BoxInst and DiscoBox in various configurations. This approach narrows the performance gap with fully supervised segmentation methods, demonstrating its practical viability and robustness in diverse scenarios.
The paper's unique contribution lies in being the first to address box-supervised instance segmentation using a level set-based method, which traditionally required fully annotated training samples or separate auxiliary tasks to generate pseudo-labels. The framework proposed reduces the complexity and resource requirements typically associated with training instance segmentation models while maintaining competitive accuracy levels.
The implication of utilizing level set evolution alongside modern deep learning architectures raises interesting prospects for future research. The method's capacity to effectively operate with less supervision points to broader applications where detailed annotations are financially or logistically impractical. Additionally, exploring alternative formulations of the energy function or various input feature configurations could further improve segmentation quality and efficiency.
In conclusion, this research provides a compelling advancement in the field of instance segmentation through innovative integration of classical methods with machine learning, paving the way for cost-effective applications and complex improvements in machine vision systems. This paper not only highlights a strong numerical performance across various benchmarks but also suggests further investigations into diverse feature interactions and potential cross-domain application scenarios.