Open-World Recognition (OWR) is an emerging field that makes a machine
learning model competent in rejecting the unknowns, managing them, and
incrementally adding novel samples to the base knowledge. However, this broad
objective is not practical for an agent that works on a specific task. Not all
rejected samples will be used for learning continually in the future. Some
novel images in the open environment may not belong to the domain of interest.
Hence, identifying the unknown in the domain of interest is essential for a
machine learning model to learn merely the important samples. In this study, we
propose an evaluation protocol for estimating a model's capability in
separating unknown in-domain (ID) and unknown out-of-domain (OOD). We evaluated
using three approaches with an unknown domain and demonstrated the possibility
of identifying the domain of interest using the pre-trained parameters through
traditional transfer learning, Automated Machine Learning (AutoML), and Nearest
Class Mean (NCM) classifier with First Integer Neighbor Clustering Hierarchy
(FINCH). We experimented with five different domains: garbage, food, dogs,
plants, and birds. The results show that all approaches can be used as an
initial baseline yielding a good accuracy. In addition, a Balanced Accuracy
(BACCU) score from a pre-trained model indicates a tendency to excel in one or
more domains of interest. We observed that MobileNetV3 yielded the highest
BACCU score for the garbage domain and surpassed complex models such as the
transformer network. Meanwhile, our results also suggest that a strong
representation in the pre-trained model is important for identifying unknown
classes in the same domain. This study could open the bridge toward open-world
recognition in domain-specific tasks where the relevancy of the unknown classes
is vital.

开放环境识别（OWR）是一个新兴领域，使得机器学习模型能够拒绝未知样本，并进行管理，逐步将新样本添加到基础知识。本研究提出了一个评估协议，用于估计模型在内域未知类和外域未知类之间分离能力，通过传统迁移学习、自动化机器学习（AutoML）和最近类均值（NCM）分类器与 First Integer Neighbor Clustering Hierarchy（FINCH）相结合的方法，通过对垃圾、食品、狗、植物和鸟类等五个不同领域进行实验，结果表明所有方法都可以作为一个良好的准确性基线，并且预训练模型的平衡准确率（BACCU）得分有可能在一个或多个感兴趣领域中表现出色，同时强调了预训练模型中的有效表示对于识别相同领域的未知类很重要，进一步拓展了开放环境识别在领域特定任务中的应用前景。

开放式识别中未知类别领域识别模型评估：一个提案

Model Evaluation for Domain Identification of Unknown Classes in  Open-World Recognition: A Proposal

As researchers strive to narrow the gap between machine intelligence and
human through the development of artificial intelligence technologies, it is
imperative that we recognize the critical importance of trustworthiness in
open-world, which has become ubiquitous in all aspects of daily life for
everyone. However, several challenges may create a crisis of trust in current
artificial intelligence systems that need to be bridged: 1) Insufficient
explanation of predictive results; 2) Inadequate generalization for learning
models; 3) Poor adaptability to uncertain environments. Consequently, we
explore a neural program to bridge trustworthiness and open-world learning,
extending from single-modal to multi-modal scenarios for readers. 1) To enhance
design-level interpretability, we first customize trustworthy networks with
specific physical meanings; 2) We then design environmental well-being
task-interfaces via flexible learning regularizers for improving the
generalization of trustworthy learning; 3) We propose to increase the
robustness of trustworthy learning by integrating open-world recognition losses
with agent mechanisms. Eventually, we enhance various trustworthy properties
through the establishment of design-level explainability, environmental
well-being task-interfaces and open-world recognition programs. These designed
open-world protocols are applicable across a wide range of surroundings, under
open-world multimedia recognition scenarios with significant performance
improvements observed.

研究人员通过发展人工智能技术，努力缩小机器智能与人类之间的差距，迫切意识到信任度在无所不在的开放世界中的重要性。本文探讨了一种神经程序，以在单模态和多模态场景下，从设计可解释性、环境福祉任务界面和开放世界识别程序角度增强各种可信赖属性，以及在开放世界多媒体识别场景中观察到的显著性能改进。

增强可解释性、泛化和鲁棒性的探索性神经方法：连接可信度与开放世界学习

Bridging Trustworthiness and Open-World Learning: An Exploratory Neural  Approach for Enhancing Interpretability, Generalization, and Robustness

We tackle the problem of threshold calibration for open-world recognition by
incorporating representation compactness measures into clustering. Unlike the
open-set recognition which focuses on discovering and rejecting the unknown,
open-world recognition learns robust representations that are generalizable to
disjoint unknown classes at test time. Our proposed method is based on two key
observations: (i) representation structures among neighbouring images in high
dimensional visual embedding spaces have strong self-similarity which can be
leveraged to encourage transferability to the open world, (ii) intra-class
embedding structures can be modeled with the marginalized von Mises-Fisher
(vMF) probability, whose correlation with the true positive rate is
dataset-invariant. Motivated by these, we design a unified framework centered
around a graph neural network (GNN) to jointly predict the pseudo-labels and
the vMF concentrations which indicate the representation compactness. These
predictions can be converted into statistical estimations for recognition
accuracy, allowing more robust calibration of the distance threshold to achieve
target utility for the open-world classes. Results on a variety of visual
recognition benchmarks demonstrate the superiority of our method over
traditional posthoc calibration methods for the open world, especially under
distribution shift.

通过将表示紧凑性度量值集成到聚类中，我们处理开放世界识别的阈值校准问题，并提出了一种基于图神经网络的统一框架来预测假标签和 vMF 浓度，以实现更为稳健的距离阈值校准。