Unknown examples that are unseen during training often appear in real-world machine learning tasks, and an intelligent self-learning system should be able to distinguish between known and unknown examples. Accordingly, open set recognition (OSR), which addresses the problem of classifying knowns and identifying unknowns, has recently been highlighted. However, conventional deep neural networks using a softmax layer are vulnerable to overgeneralization, producing high confidence scores for unknowns. In this paper, we propose a simple OSR method based on the intuition that OSR performance can be maximized by setting strict and sophisticated decision boundaries that reject unknowns while maintaining satisfactory classification performance on knowns. For this purpose, a novel network structure is proposed, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network feature extractor. Here, the OVRN is a simple feed-forward neural network that enhances the ability to reject nonmatches by learning class-specific discriminative features. Furthermore, the collective decision score is modeled by combining the multiple decisions reached by the OVRNs to alleviate overgeneralization. Extensive experiments were conducted on various datasets, and the experimental results showed that the proposed method performed significantly better than the state-of-the-art methods by effectively reducing overgeneralization.
ResLPR: A LiDAR Data Restoration Network and Benchmark for Robust Place Recognition Against Weather Corruptions
College of Intelligence Science and Technology（智能科学与技术学院）；National University of Defense Technology（国防科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；University of Science and Technology of China（中国科学技术大学）
Latent Embeddings for Collective Activity Recognition
School of Electronics and Information Technology, Sun Yat-Sen University（中山大学电子与信息工程学院）；School of Data and Computer Science, Sun Yat-Sen University（中山大学数据与计算机学院）
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
Carnegie Mellon University（卡内基梅隆大学）；Harvard University（哈佛大学）；CIOMP, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；Graduate School at Shenzhen, Tsinghua University（清华大学深圳研究生院）；The Hong Kong Polytechnic University（香港理工大学）
