Glocal Energy-based Learning for Few-Shot Open-Set Recognition
Date
2023
Authors
Wang, H.
Pang, G.
Wang, P.
Zhang, L.
Wei, W.
Zhang, Y.
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Advisors
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Conference paper
Citation
Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023, vol.2023-June, pp.7507-7516
Statement of Responsibility
Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang
Conference Name
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (17 Jun 2023 - 24 Jun 2023 : Vancouver, Canada)
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 closedset classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of openset samples, our model leverages both class-wise and pixelwise 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 pixelwise 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.¹
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©2023 IEEE