Classifying patterns of known classes and rejecting ambiguous and novel (also
called as out-of-distribution (OOD)) inputs are involved in open world pattern
recognition. Deep neural network models usually excel in closed-set
classification while performing poorly in rejecting OOD. To tackle this
problem, numerous methods have been designed to perform open set recognition
(OSR) or OOD rejection/detection tasks. Previous methods mostly take
post-training score transformation or hybrid models to ensure low scores on OOD
inputs while separating known classes. In this paper, we attempt to build a
unified framework for building open set classifiers for both classification and
OOD rejection. We formulate the open set recognition of $ K $-known-class as a
$ (K + 1) $-class classification problem with model trained on known-class
samples only. By decomposing the $ K $-class problem into $ K $ one-versus-all
(OVA) binary classification tasks and binding some parameters, we show that
combining the scores of OVA classifiers can give $ (K + 1) $-class posterior
probabilities, which enables classification and OOD rejection in a unified
framework. To maintain the closed-set classification accuracy of the OVA
trained classifier, we propose a hybrid training strategy combining OVA loss
and multi-class cross-entropy loss. We implement the OVA framework and hybrid
training strategy on the recently proposed convolutional prototype network.
Experiments on popular OSR and OOD detection datasets demonstrate that the
proposed framework, using a single multi-class classifier, yields competitive
performance in closed-set classification, OOD detection, and misclassification
detection.

通过使用统一框架和混合训练策略，本文在开放世界模式识别中建立了一个能够同时进行分类和拒绝未知输入的开放集分类器，实验证明该方法在闭集分类、未知输入检测和错误分类检测中表现竞争性。

统一的分类和拒绝：一对多的框架

Unified Classification and Rejection: A One-versus-All Framework

Though much progress has been achieved in single-image 3D human recovery,
estimating 3D model for in-the-wild images remains a formidable challenge. The
reason lies in the fact that obtaining high-quality 3D annotations for
in-the-wild images is an extremely hard task that consumes enormous amount of
resources and manpower. To tackle this problem, previous methods adopt a hybrid
training strategy that exploits multiple heterogeneous types of annotations
including 3D and 2D while leaving the efficacy of each annotation not
thoroughly investigated. In this work, we aim to perform a comprehensive study
on cost and effectiveness trade-off between different annotations.
Specifically, we focus on the challenging task of in-the-wild 3D human recovery
from single images when paired 3D annotations are not fully available. Through
extensive experiments, we obtain several observations: 1) 3D annotations are
efficient, whereas traditional 2D annotations such as 2D keypoints and body
part segmentation are less competent in guiding 3D human recovery. 2) Dense
Correspondence such as DensePose is effective. When there are no paired
in-the-wild 3D annotations available, the model exploiting dense correspondence
can achieve 92% of the performance compared to a model trained with paired 3D
data. We show that incorporating dense correspondence into in-the-wild 3D human
recovery is promising and competitive due to its high efficiency and relatively
low annotating cost. Our model trained with dense correspondence can serve as a
strong reference for future research.

本文提出一种全面的方法，研究了不同类型标注成本和效果的权衡，特别是探究了在缺乏匹配的实境 3D 标注数据情况下，利用 Dense Correspondence（密集对应）实现在单一图像中实现 3D 人类重构，发现与传统 2D 标注相比，3D 标注是有效的，但 Dense Correspondence 也同样具有高效性和较低的成本。