Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (2104.01893v2)

Abstract: Prototype learning is extensively used for few-shot segmentation. Typically, a single prototype is obtained from the support feature by averaging the global object information. However, using one prototype to represent all the information may lead to ambiguities. In this paper, we propose two novel modules, named superpixel-guided clustering (SGC) and guided prototype allocation (GPA), for multiple prototype extraction and allocation. Specifically, SGC is a parameter-free and training-free approach, which extracts more representative prototypes by aggregating similar feature vectors, while GPA is able to select matched prototypes to provide more accurate guidance. By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation. In addition, our network can easily generalize to k-shot segmentation with substantial improvement and no additional computational cost. In particular, our evaluations on COCO demonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot segmentation.

• The paper introduces ASGNet, a novel architecture utilizing Superpixel-guided Clustering and Guided Prototype Allocation to generate adaptive prototypes.
• It dynamically adjusts prototypes based on object scale and shape, enhancing segmentation performance without additional computational overhead.
• ASGNet achieves competitive mIoU improvements on Pascal-5^i and COCO-20^i benchmarks, outperforming existing few-shot methods by over 5%.

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

The paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" addresses the challenges in few-shot image segmentation by proposing a novel method to enhance the capability of models to generalize from limited samples. The focus of the paper is on leveraging adaptive prototype methods that can adjust to the requirements of various segmentation tasks, specifically catering to the diversity in object size, shape, and occlusion.

Overview of Methodology

The central contribution of the paper is the introduction of the Adaptive Superpixel-guided Network (ASGNet), which is composed of two innovative modules: Superpixel-guided Clustering (SGC) and Guided Prototype Allocation (GPA). These components are strategically designed to extract and allocate prototypes more effectively for few-shot segmentation tasks.

Superpixel-guided Clustering (SGC):

• SGC operates by aggregating similar feature vectors derived from the support image to generate multiple prototypes.
• This parameter-free and training-free approach increases the robustness of the prototype generation by ensuring prototypes are representative of the feature diversity within an object.
• The clustering process adapts to the object scale by employing a dynamic number of prototypes, calculated according to the object's size in the feature space.

Guided Prototype Allocation (GPA):

• GPA focuses on selecting the most relevant prototype for each query pixel through an attention-like mechanism.
• It computes the cosine similarity between query features and the prototypes and allocates those that provide the most accurate representation for each pixel.
• This mechanism enhances the segmentation accuracy by ensuring that the prototype allocation is adaptive to the object shape and visibility within the query images.

Practical and Theoretical Implications

The ASGNet demonstrates practical superiority by achieving competitive results on challenging benchmarks like the Pascal-$5^i$ and COCO-$20^i$ datasets. The integration of the SGC and GPA modules allows ASGNet to surpass existing methods significantly—in some cases by over 5% in mIoU (mean Intersection over Union) for 5-shot settings. This is indicative of its enhanced capability to manage variations across diverse datasets without additional computational overhead.

From a theoretical perspective, this paper contributes to the understanding of how prototype representation in few-shot learning can be dynamically adjusted based on the object's inherent properties. The idea of using multiple, content-adaptive prototypes aligns well with human perceptual capabilities, which adjust to focus on critical object parts based on context.

Numerical Results and Comparisons

The results show a substantive improvement in segmentation performance, evidenced by mIoUs of approximately 64.36% on Pascal-$5^i$ and 42.48% on COCO-$20^i$ in 5-shot tests, highlighting ASGNet's ability to handle a variety of challenges in few-shot segmentation scenarios. These enhancements are observed alongside a consistently lower parameter count, reflecting the method's efficiency.

Future Directions

The framework provided by ASGNet and its modules opens pathways for future research in prototype learning and allocation methods not just limited to image segmentation but extensible to other domains in computer vision and artificial intelligence. Exploring further enhancements to the clustering mechanism and attention-based allocation can lead to models capable of even finer adaptivity and feature discrimination.

In summary, the paper offers a well-grounded approach to improve few-shot segmentation by using adaptive prototypes, presenting clear advances in both accuracy and efficiency. This work sets the stage for subsequent research in advancing few-shot learning capabilities, potentially leading to broader applications in dynamic and diverse environments.