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Proceedings Paper
Computer Science, Artificial Intelligence
Mamshad Nayeem Rizve et al.
Summary: This research addresses the open-world semi-supervised learning problem, which does not assume that labeled and unlabeled data come from the same data distribution. By utilizing pairwise similarity loss to discover novel classes and transforming the problem into a standard SSL problem, it achieves improved performance.
COMPUTER VISION, ECCV 2022, PT XXXI
(2022)
Article
Computer Science, Artificial Intelligence
Kai Han et al.
Summary: In this paper, a method called AutoNovel is proposed to discover novel classes in an image collection. By utilizing self-supervised learning, ranking statistics, and optimizing a joint objective function, both supervised classification of labeled data and clustering of unlabeled data are improved. Furthermore, a method to estimate the number of new classes is also proposed.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Enrico Fini et al.
Summary: This paper introduces a novel approach for Novel Class Discovery (NCD) by using a Unified Objective function (UNO) to enhance synergy between supervised and unsupervised learning. By utilizing a multi-view self-labeling strategy to generate pseudo-labels, it achieves a single classification objective operating on both known and unknown classes.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
K. J. Joseph et al.
Summary: Humans have a natural instinct to identify unknown object instances and their curiosity aids in learning about them. The study introduces a novel problem of Open World Object Detection, where a model is tasked to identify unknown objects without explicit supervision and incrementally learn them. The proposed solution ORE shows effectiveness in achieving Open World objectives and reducing confusion in incremental object detection.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhun Zhong et al.
Summary: In this paper, the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes is addressed. By mixing labeled examples with unlabeled examples and producing more credible pseudo-labels, the proposed OpenMix method prevents overfitting on unlabeled samples. Additionally, using high class-probability unlabeled examples as reliable anchors improves accuracy and enables finer object relations among new classes.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Computer Science, Artificial Intelligence
Takeru Miyato et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Bin Liu et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW)
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Hu Xu et al.
WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaiming He et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Article
Computer Science, Artificial Intelligence
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Ross Girshick
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2015)
Article
Computer Science, Artificial Intelligence
Walter J. Scheirer et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2013)
