Fine-grained open-set recognition (FineOSR) aims to recognize images
belonging to classes with subtle appearance differences while rejecting images
of unknown classes. A recent trend in OSR shows the benefit of generative
models to discriminative unknown detection. As a type of generative model,
energy-based models (EBM) are the potential for hybrid modeling of generative
and discriminative tasks. However, most existing EBMs suffer from density
estimation in high-dimensional space, which is critical to recognizing images
from fine-grained classes. In this paper, we explore the low-dimensional latent
space with energy-based prior distribution for OSR in a fine-grained visual
world. Specifically, based on the latent space EBM, we propose an
attribute-aware information bottleneck (AIB), a residual attribute feature
aggregation (RAFA) module, and an uncertainty-based virtual outlier synthesis
(UVOS) module to improve the expressivity, granularity, and density of the
samples in fine-grained classes, respectively. Our method is flexible to take
advantage of recent vision transformers for powerful visual classification and
generation. The method is validated on both fine-grained and general visual
classification datasets while preserving the capability of generating
photo-realistic fake images with high resolution.

本文探讨了在细粒度视觉世界中基于低维潜在空间与能量模型的先验分布的开放集识别问题，提出了一个属性感知信息瓶颈（AIB）模块、一个残差属性特征聚合（RAFA）模块和一个基于不确定性的虚拟异常点合成（UVOS）模块，分别改善了精细化类别样本的表达能力、细粒度和密度。该方法可利用近期的视觉变换器进行强大的视觉分类和生成，并在精细化和通用的视觉分类数据集上得到验证，同时保持了生成高分辨率逼真图像的能力。