无需人工标注，自动构建类别层级实现更鲁棒的开放集识别。
Data-Driven Hierarchical Open Set Recognition
- 用约束凝聚聚类在嵌入空间自动生成已知类的层次结构。
- 在AwA2数据集上达到0.82的AUC ROC和0.85的效用得分。
- 引入新指标衡量分类一致性，适合需理解未知类关系的场景。
本文提出一种新颖的数据驱动分层开放集识别方法，用于提升机器人与计算机视觉中的鲁棒感知。该方法利用约束凝聚聚类，在嵌入空间中自动构建已知类的层次结构，无需人工关系信息。实验在Animals with Attributes 2（AwA2）数据集上进行，取得0.82的AUC ROC得分和0.85的效用分数。研究提出两种分类策略（基于得分与遍历式），并引入新的集中中心性（CC）度量以评估分层分类的一致性。尽管精度未超越现有模型，但该方法通过自动生成的层次结构提供了关于未知类的额外信息，仅需常规监督学习所需数据，且提出了类别集中中心性（CCC）指标用于评估未知类定位的一致性。未来工作将聚焦于提升准确性、验证CC指标，并扩展至ImageNet的大规模开放集分类协议。
原文摘要 · Abstract (English)
This paper presents a novel data-driven hierarchical approach to open set recognition (OSR) for robust perception in robotics and computer vision, utilizing constrained agglomerative clustering to automatically build a hierarchy of known classes in embedding space without requiring manual relational information. The method, demonstrated on the Animals with Attributes 2 (AwA2) dataset, achieves competitive results with an AUC ROC score of 0.82 and utility score of 0.85, while introducing two classification approaches (score-based and traversal-based) and a new Concentration Centrality (CC) metric for measuring hierarchical classification consistency. Although not surpassing existing models in accuracy, the approach provides valuable additional information about unknown classes through automatically generated hierarchies, requires no supplementary information beyond typical supervised model requirements, and introduces the Class Concentration Centrality (CCC) metric for evaluating unknown class placement consistency, with future work aimed at improving accuracy, validating the CC metric, and expanding to Large-Scale Open-Set Classification Protocols for ImageNet.
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