Symmetrical Joint Learning Support-query Prototypes for Few-shot Segmentation

TL;DR

Sym-Net introduces a symmetrical joint learning framework for few-shot segmentation that improves intra-class variation handling and generalization by balancing support and query prototype learning, incorporating novel modules for prototype aggregation, localization, and spatial relationship modeling.

Contribution

The paper presents a novel symmetrical learning approach for support-query prototypes in FSS, including modules for prototype aggregation, localization, and multi-scale spatial relationship capture.

Findings

Outperforms state-of-the-art models on FSS benchmarks.

Effectively handles intra-class variation and unseen classes.

Enhances segmentation accuracy with limited data.

Abstract

We propose Sym-Net, a novel framework for Few-Shot Segmentation (FSS) that addresses the critical issue of intra-class variation by jointly learning both query and support prototypes in a symmetrical manner. Unlike previous methods that generate query prototypes solely by matching query features to support prototypes, which is a form of bias learning towards the few-shot support samples, Sym-Net leverages a balanced symmetrical learning approach for both query and support prototypes, ensuring that the learning process does not favor one set (support or query) over the other. One of main modules of Sym-Net is the visual-text alignment-based prototype aggregation module, which is not just query-guided prototype refinement, it is a jointly learning from both support and query samples, which makes the model beneficial for handling intra-class discrepancies and allows it to generalize better to new, unseen classes. Specifically, a parameter-free prior mask generation module is designed to accurately localize both local and global regions of the query object by using sliding windows of different sizes and a self-activation kernel to suppress incorrect background matches. Additionally, to address the information loss caused by spatial pooling during prototype learning, a top-down hyper-correlation module is integrated to capture multi-scale spatial relationships between support and query images. This approach is further jointly optimized by implementing a co-optimized hard triplet mining strategy. Experimental results show that the proposed Sym-Net outperforms state-of-the-art models, which demonstrates that jointly learning support-query prototypes in a symmetrical manner for FSS offers a promising direction to enhance segmentation performance with limited annotated data.