PSS: Progressive Sample Selection for Open-World Visual Representation Learning

Cao, Tianyue; Wang, Yongxin; Xing, Yifan; Xiao, Tianjun; He, Tong; Zhang, Zheng; Zhou, Hao; Tighe, Joseph

doi:10.1007/978-3-031-19821-2_16

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is costly to re-train the model every time. Under such application demand, a series of open-world recognition methods have been proposed, which aim to continuously detect and add new classes encountered [115][116][117].…”

Section: Extension Tasksmentioning

confidence: 99%

“…Bendale and Boult [115] firstly proposed the open-world recognition concept, and also extended the nearest class mean classifiers to the open-world recognition task. Cao et al [116] proposed a progressive transductive method, which selected unlabeled new samples and provided them pseudo labels based on clustering results for updating the feature prototypes. Wu et al [117] replied on graph representation and learning for predicting and utilizing new-class samples, where a graph network was used for extrapolate embeddings for features extracted from new data based on a feature-level graph and a prediction network was used for predicting pseudo labels for the new features.…”

Section: Extension Tasksmentioning

confidence: 99%

See 1 more Smart Citation

A study on open set recognition methods

Sun¹

View full text Add to dashboard Cite

Ling Keck-Voon. He constantly encouraged and supported me throughout my Ph.D. career. He has helped me a lot in how to do academic research, in-depth thinking, and manuscript writing. I would like to thank Professor Lin Guoshen for his patient guidance and for leading me into the wonderful world of deep learning. Also, I would like to thank Nanyang Technological University, National Research Foundation Singapore, and SMRT Corporation for funding my education and research.

show abstract

Section: Extension Tasksmentioning

confidence: 99%