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

• URL: http://arxiv.org/abs/2304.11855v1
• Date: Mon, 24 Apr 2023 07:06:50 GMT
• Title: Glocal Energy-based Learning for Few-Shot Open-Set Recognition
• Authors: Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang
• Abstract summary: Few-shot open-set recognition (FSOR) is a challenging task of great practical value. We propose a novel energy-based hybrid model for FSOR. Experiments on three standard FSOR datasets show the superior performance of our model.
• Score: 57.84234213466372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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