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TASK-AGNOSTIC OPEN-SET PROTOTYPE FOR FEW-SHOT OPEN-SET RECOGNITION

Byeonggeun Kim, Jun-Tae Lee, Kyuhong Shim, Simyung Chang

  • SPS
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Poster 11 Oct 2023

In few-shot open-set recognition (FSOSR), a network learns to recognize closed-set samples with a few support samples while rejecting open-set samples with no class cue. Unlike conventional OSR, the FSOSR considers more practical open worlds where a closed-set class can be selected as an open-set class in another testing (task) and vice versa. Existing FSOSR methods have commonly represented the open set with task-dependent extra modules. These modules decently handle the varied closed and open classes but accompany inevitable complexity increase. This paper shows that a single open-set prototype can represent open-set samples when it satisfies a specific relation in metric space: closest to open-set, and simultaneously second nearest to close-set. We propose a task-agnostic open-set prototype with distance scaling factors and design loss terms. We extensively analyze the proposed components to demonstrate their importance. Our method achieves state-of-the-art results on miniImageNet and tieredImageNet, respectively, without task-dependent extra modules.

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    Members: Free
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    Non-members: $15.00