Glocal Energy-based Learning for Few-Shot Open-Set Recognition

TL;DR

This paper introduces a novel energy-based hybrid model for few-shot open-set recognition that combines class-wise and pixel-wise features to effectively distinguish known from unknown samples, demonstrating superior performance on standard datasets.

Contribution

The paper proposes a new energy-based hybrid model that integrates class-wise and pixel-wise features for improved open-set detection in few-shot learning scenarios.

Findings

Superior performance on three standard FSOR datasets

Effective detection of open-set samples using combined energy scores

Outperforms existing methods in open-set recognition accuracy

Abstract

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model.