ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification

TL;DR

ProtoConNet introduces a novel approach for open-set few-shot image classification by integrating background context and prototypical alignment, significantly improving the model's ability to distinguish known from unknown classes with limited data.

Contribution

It proposes a new method combining background augmentation, contextual refinement, and prototype alignment to enhance open-set few-shot classification performance.

Findings

Outperforms existing methods on two datasets

Enhances robustness in recognizing unknown classes

Improves feature diversity and representation quality

Abstract

Open-set few-shot image classification aims to train models using a small amount of labeled data, enabling them to achieve good generalization when confronted with unknown environments. Existing methods mainly use visual information from a single image to learn class representations to distinguish known from unknown categories. However, these methods often overlook the benefits of integrating rich contextual information. To address this issue, this paper proposes a prototypical augmentation and alignment method, termed ProtoConNet, which incorporates background information from different samples to enhance the diversity of the feature space, breaking the spurious associations between context and image subjects in few-shot scenarios. Specifically, it consists of three main modules: the clustering-based data selection (CDS) module mines diverse data patterns while preserving core features; the contextual-enhanced semantic refinement (CSR) module builds a context dictionary to integrate into image representations, which boosts the model's robustness in various scenarios; and the prototypical alignment (PA) module reduces the gap between image representations and class prototypes, amplifying feature distances for known and unknown classes. Experimental results from two datasets verified that ProtoConNet enhances the effectiveness of representation learning in few-shot scenarios and identifies open-set samples, making it superior to existing methods.