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Sparse Representation-Based Open Set Recognition,
PAMI(39), No. 8, August 2017, pp. 1690-1696.
IEEE DOI
1707
Not all classes presented during testing are known during training.
Animals, Data models, Image reconstruction, Indexes,
Pattern analysis, Testing, Training, Open set recognition,
extreme value theory, sparse, representation-based, classification
BibRef
de Oliveira Werneck, R.[Rafael],
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Learning cost function for graph classification with open-set methods,
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Elsevier DOI
1912
Graph matching, Cost learning, SVM, Open-set recognition
BibRef
Earlier:
Learning Cost Functions for Graph Matching,
SSSPR18(345-354).
Springer DOI
1810
BibRef
Dang, S.A.[Sih-Ang],
Cao, Z.J.[Zong-Jie],
Cui, Z.Y.[Zong-Yong],
Pi, Y.M.[Yi-Ming],
Liu, N.Y.[Neng-Yuan],
Open Set Incremental Learning for Automatic Target Recognition,
GeoRS(57), No. 7, July 2019, pp. 4445-4456.
IEEE DOI
1907
Classifier with rejection option.
Training, Target recognition, Learning systems,
Computational modeling, Support vector machines,
open set recognition (OSR)
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Geng, C.X.[Chuan-Xing],
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Elsevier DOI
2003
Convolutional prototype learning,
Generalized zero-shot Learning, Open set recognition
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Domain Adaptation Network for Cross-Scene Classification,
GeoRS(55), No. 8, August 2017, pp. 4441-4456.
IEEE DOI
1708
Earth, Feature extraction, Feeds,
Machine learning, Neural networks, Remote sensing,
Cross-scene classification, distribution mismatch,
domain adaptation, multisensor data, pretrained, convolutional,
neural, network, (CNN)
BibRef
Adayel, R.[Reham],
Bazi, Y.[Yakoub],
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Deep Open-Set Domain Adaptation for Cross-Scene Classification based
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Positive-unlabeled learning for open set domain adaptation,
PRL(136), 2020, pp. 198-204.
Elsevier DOI
2008
Deep learning, Image classification,
Domain adaptation, Open set recognition, Positive-Unlabelled learning
BibRef
Fu, Y.,
Wang, X.,
Dong, H.,
Jiang, Y.G.,
Wang, M.,
Xue, X.,
Sigal, L.,
Vocabulary-Informed Zero-Shot and Open-Set Learning,
PAMI(42), No. 12, December 2020, pp. 3136-3152.
IEEE DOI
2011
Semantics, Vocabulary, Training data, Prototypes, Image recognition,
Visualization, Learning systems, Vocabulary-informed learning,
zero-shot learning
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Liu, S.J.[Sheng-Jie],
Shi, Q.[Qian],
Zhang, L.P.[Liang-Pei],
Few-Shot Hyperspectral Image Classification With Unknown Classes
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GeoRS(59), No. 6, June 2021, pp. 5085-5102.
IEEE DOI
2106
Hyperspectral imaging, Image recognition, Image reconstruction,
Machine learning, Training, Data models,
open-set recognition
BibRef
Cevikalp, H.[Hakan],
Uzun, B.[Bedirhan],
Köpüklü, O.[Okan],
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Deep compact polyhedral conic classifier for open and closed set
recognition,
PR(119), 2021, pp. 108080.
Elsevier DOI
2106
Polyhedral conic classifier, Deep learning,
Open set recognition, Image classification, Anomaly detection
BibRef
Zhou, H.H.[Hao-Hong],
Azzam, M.[Mohamed],
Zhong, J.[Jian],
Liu, C.[Cheng],
Wu, S.[Si],
Wong, H.S.[Hau-San],
Knowledge Exchange Between Domain-Adversarial and Private Networks
Improves Open Set Image Classification,
IP(30), 2021, pp. 5807-5818.
IEEE DOI
2106
Adaptation models, Training, Task analysis, Knowledge engineering,
Computational modeling, Data models, Benchmark testing,
image classification
BibRef
Feng, Z.[Zeyu],
Xu, C.[Chang],
Tao, D.C.[Da-Cheng],
Open-Set Hypothesis Transfer With Semantic Consistency,
IP(30), 2021, pp. 6473-6484.
IEEE DOI
2107
Adaptation models, Data models, Predictive models, Semantics,
Training, Standards, Entropy, Open-set, domain adaptation,
consistency regularization
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Shermin, T.[Tasfia],
Lu, G.J.[Guo-Jun],
Teng, S.W.[Shyh Wei],
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Adversarial Network With Multiple Classifiers for Open Set Domain
Adaptation,
MultMed(23), 2021, pp. 2732-2744.
IEEE DOI
2109
Adaptation models, Training, Loss measurement, Generators,
Computational modeling, Data models, Task analysis,
multi-classifier based weighting module
BibRef
Geng, C.X.[Chuan-Xing],
Huang, S.J.[Sheng-Jun],
Chen, S.C.[Song-Can],
Recent Advances in Open Set Recognition: A Survey,
PAMI(43), No. 10, October 2021, pp. 3614-3631.
IEEE DOI
2109
Survey, Open Set Recognition. Training, Testing, Task analysis, Semantics, Face recognition,
Data visualization, Open set recognition/classification,
one-shot learning
BibRef
Park, J.[Jaewoo],
Low, C.Y.[Cheng Yaw],
Teoh, A.B.J.[Andrew Beng Jin],
Divergent Angular Representation for Open Set Image Recognition,
IP(31), 2022, pp. 176-189.
IEEE DOI
2112
Data models, Training, Prototypes, Semantics, Loss measurement,
Image recognition, Robustness, Open set recognition,
representation learning
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Chambers, L.[Lorraine],
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DeepStreamOS: Fast open-Set classification for convolutional neural
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PRL(154), 2022, pp. 75-82.
Elsevier DOI
2202
Open-Set classification, Out-of-Distribution,
Deep neural networks, Convolutional neural networks,
Streaming machine learning
BibRef
Yang, H.M.[Hong-Ming],
Zhang, X.Y.[Xu-Yao],
Yin, F.[Fei],
Yang, Q.[Qing],
Liu, C.L.[Cheng-Lin],
Convolutional Prototype Network for Open Set Recognition,
PAMI(44), No. 5, May 2022, pp. 2358-2370.
IEEE DOI
2204
Prototypes, Training, Feature extraction, Robustness, Task analysis,
Biological neural networks, Brain modeling, Open-set recognition,
generative model
BibRef
Zhang, Y.[Yabin],
Deng, B.[Bin],
Tang, H.[Hui],
Zhang, L.[Lei],
Jia, K.[Kui],
Unsupervised Multi-Class Domain Adaptation:
Theory, Algorithms, and Practice,
PAMI(44), No. 5, May 2022, pp. 2775-2792.
IEEE DOI
2204
Training, Training data, Task analysis, Testing, Machine learning,
Adaptation models, Standards, Domain adaptation,
partial or open set domain adaptation
BibRef
Yuan, Y.[Yuan],
He, X.X.[Xin-Xing],
Jiang, Z.[Zhiyu],
Adaptive open domain recognition by coarse-to-fine prototype-based
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PR(128), 2022, pp. 108657.
Elsevier DOI
2205
Open domain recognition, Image classification,
Adaptive openness, Prototype learning, Unknown class recognition
BibRef
Bevandic, P.[Petra],
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Oric, M.[Marin],
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Dense open-set recognition based on training with noisy negative
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IVC(124), 2022, pp. 104490.
Elsevier DOI
2208
Dense prediction, Semantic segmentation,
Dense open-set recognition, Outlier detection
BibRef
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Ghio, S.[Selenia],
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Martorella, M.[Marco],
Proportional Similarity-Based Openmax Classifier for Open Set
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RS(14), No. 18, 2022, pp. xx-yy.
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Tian, Y.H.[Yong-Hong],
Adversarial Reciprocal Points Learning for Open Set Recognition,
PAMI(44), No. 11, November 2022, pp. 8065-8081.
IEEE DOI
2210
Training, Cats, Prototypes, Task analysis, Pattern recognition,
Deep learning, Uncertainty, Open set recognition,
generative adversarial learning
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Chen, G.Y.[Guang-Yao],
Qiao, L.M.[Li-Meng],
Shi, Y.M.[Ye-Min],
Peng, P.X.[Pei-Xi],
Li, J.[Jia],
Huang, T.J.[Tie-Jun],
Pu, S.L.[Shi-Liang],
Tian, Y.H.[Yong-Hong],
Learning Open Set Network with Discriminative Reciprocal Points,
ECCV20(III:507-522).
Springer DOI
2012
BibRef
Cordeiro, F.R.[Filipe R.],
Sachdeva, R.[Ragav],
Belagiannis, V.[Vasileios],
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LongReMix: Robust learning with high confidence samples in a noisy
label environment,
PR(133), 2023, pp. 109013.
Elsevier DOI
2210
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Earlier: A2, A1, A3, A4, A5:
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy
Labels,
WACV21(3606-3614)
IEEE DOI
2106
Noisy label learning, Deep learning, Empirical vicinal risk,
Semi-supervised learning.
Training, Deep, Uncertainty, Annotations, Semantics,
Training data, Focusing
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Naranjo-Alcazar, J.[Javier],
Perez-Castanos, S.[Sergi],
Zuccarello, P.[Pedro],
Torres, A.M.[Ana M.],
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Cobos, M.[Maximo],
An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event
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PRL(164), 2022, pp. 40-45.
Elsevier DOI
2212
Audio Dataset, Classification, Few-Shot Learning,
Machine Listening, Open-set Recognition, Sound Processing
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Sun, J.Y.[Jia-Yin],
Wang, H.[Hong],
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MoEP-AE: Autoencoding Mixtures of Exponential Power Distributions for
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CirSysVideo(33), No. 1, January 2023, pp. 312-325.
IEEE DOI
2301
Feature extraction, Task analysis, Training, Power distribution, Sun,
Decoding, Gaussian distribution, Open-set recognition, autoencoder,
exponential power distribution
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Huang, H.Z.[Hong-Zhi],
Wang, Y.[Yu],
Hu, Q.H.[Qing-Hua],
Cheng, M.M.[Ming-Ming],
Class-Specific Semantic Reconstruction for Open Set Recognition,
PAMI(45), No. 4, April 2023, pp. 4214-4228.
IEEE DOI
2303
Image reconstruction, Manifolds, Prototypes, Semantics, Training,
Task analysis, Image recognition, Classification,
class-specific semantic reconstruction
BibRef
Xia, Z.H.[Zi-Heng],
Wang, P.H.[Peng-Hui],
Dong, G.G.[Gang-Gang],
Liu, H.W.[Hong-Wei],
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CVIU(229), 2023, pp. 103651.
Elsevier DOI
2303
Open set recognition,
Spatial location constrain prototype loss, Matching theory, Empirical risk
BibRef
Cevikalp, H.[Hakan],
Uzun, B.[Bedirhan],
Salk, Y.[Yusuf],
Saribas, H.[Hasan],
Köpüklü, O.[Okan],
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PR(138), 2023, pp. 109385.
Elsevier DOI
2303
Anomaly detection, Open set recognition, Hypersphere classifier, Deep learning
BibRef
Zhu, F.[Fei],
Zhang, X.Y.[Xu-Yao],
Wang, R.Q.[Rui-Qi],
Liu, C.L.[Cheng-Lin],
Learning by Seeing More Classes,
PAMI(45), No. 6, June 2023, pp. 7477-7493.
IEEE DOI
2305
Task analysis, Training, Measurement, Adaptation models, Reliability,
Calibration, Pattern recognition, Class augmentation,
open-environment learning
BibRef
Liu, Z.G.[Zhun-Ga],
Fu, Y.M.[Yi-Min],
Pan, Q.[Quan],
Zhang, Z.W.[Zuo-Wei],
Orientational Distribution Learning With Hierarchical Spatial
Attention for Open Set Recognition,
PAMI(45), No. 7, July 2023, pp. 8757-8772.
IEEE DOI
2306
Training, Optimization, Graphical models, Distribution functions,
Deep learning, Support vector machines, Neural networks,
orientational distribution learning
BibRef
An, Y.X.[Yue-Xuan],
Xue, H.[Hui],
Zhao, X.Y.[Xing-Yu],
Wang, J.[Jing],
From Instance to Metric Calibration:
A Unified Framework for Open-World Few-Shot Learning,
PAMI(45), No. 8, August 2023, pp. 9757-9773.
IEEE DOI
2307
Calibration, Prototypes, Task analysis, Measurement,
Noise measurement, Adaptation models, Training, Few-shot learning,
metric learning
BibRef
Huang, J.[Jin],
Prijatelj, D.[Derek],
Dulay, J.[Justin],
Scheirer, W.[Walter],
Measuring Human Perception to Improve Open Set Recognition,
PAMI(45), No. 9, September 2023, pp. 11382-11389.
IEEE DOI
2309
BibRef
Luo, Y.[Yadan],
Wang, Z.J.[Zi-Jian],
Chen, Z.X.[Zhuo-Xiao],
Huang, Z.[Zi],
Baktashmotlagh, M.[Mahsa],
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation,
PAMI(45), No. 9, September 2023, pp. 11240-11255.
IEEE DOI
2309
BibRef
Baktashmotlagh, M.[Mahsa],
Chen, T.L.[Tian-Le],
Salzmann, M.[Mathieu],
Learning to Generate the Unknowns as a Remedy to the Open-Set Domain
Shift,
WACV22(3737-3746)
IEEE DOI
2202
Adaptation models, Training data,
Benchmark testing, Data models, Task analysis,
Object Detection/Recognition/Categorization
BibRef
Long, Y.X.[Yan-Xin],
Wen, Y.[Youpeng],
Han, J.H.[Jian-Hua],
Xu, H.[Hang],
Ren, P.Z.[Peng-Zhen],
Zhang, W.[Wei],
Zhao, S.[Shen],
Liang, X.D.[Xiao-Dan],
CapDet: Unifying Dense Captioning and Open-World Detection
Pretraining,
CVPR23(15233-15243)
IEEE DOI
2309
BibRef
Zhou, H.M.[Hua-Ming],
Wu, H.B.[Hai-Bin],
Wang, A.[Aili],
Iwahori, Y.[Yuji],
Yu, X.Y.[Xiao-Yu],
Incorporating Attention Mechanism, Dense Connection Blocks, and
Multi-Scale Reconstruction Networks for Open-Set Hyperspectral Image
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RS(15), No. 18, 2023, pp. 4535.
DOI Link
2310
BibRef
Li, T.[Taotao],
Wen, Z.Y.[Zhen-Yu],
Long, Y.[Yang],
Hong, Z.[Zhen],
Zheng, S.[Shilian],
Yu, L.[Li],
Chen, B.[Bo],
Yang, X.[Xiaoniu],
Shao, L.[Ling],
The Importance of Expert Knowledge for Automatic Modulation Open Set
Recognition,
PAMI(45), No. 11, November 2023, pp. 13730-13748.
IEEE DOI
2310
BibRef
Li, J.[Jing],
Yang, L.[Liu],
Wang, Q.L.[Qi-Long],
Hu, Q.H.[Qing-Hua],
WDAN: A Weighted Discriminative Adversarial Network With Dual
Classifiers for Fine-Grained Open-Set Domain Adaptation,
CirSysVideo(33), No. 9, September 2023, pp. 5133-5147.
IEEE DOI
2310
BibRef
Jiang, G.S.[Guo-Song],
Zhu, P.F.[Peng-Fei],
Wang, Y.[Yu],
Hu, Q.H.[Qing-Hua],
OpenMix+: Revisiting Data Augmentation for Open Set Recognition,
CirSysVideo(33), No. 11, November 2023, pp. 6777-6787.
IEEE DOI
2311
BibRef
Sun, J.Y.[Jia-Yin],
Dong, Q.[Qiulei],
Conditional feature generation for transductive open-set recognition
via dual-space consistent sampling,
PR(146), 2024, pp. 110046.
Elsevier DOI
2311
Open-set recognition, Transductive learning, Generative learning
BibRef
Nag, S.[Sayak],
Raychaudhuri, D.S.[Dripta S.],
Paul, S.[Sujoy],
Roy-Chowdhury, A.K.[Amit K.],
Reconstruction Guided Meta-Learning for Few Shot Open Set Recognition,
PAMI(45), No. 12, December 2023, pp. 15394-15405.
IEEE DOI
2311
BibRef
Park, J.[Jaewoo],
Park, H.[Hojin],
Jeong, E.[Eunju],
Teoh, A.B.J.[Andrew Beng Jin],
Understanding open-set recognition by Jacobian norm and inter-class
separation,
PR(145), 2024, pp. 109942.
Elsevier DOI
2311
Open-set recognition, Representation learning, Metric-learning,
Object classification
BibRef
Li, X.[Xiao],
Fang, M.[Min],
Zhai, Z.B.[Zhi-Bo],
Joint Feature Generation and Open-set Prototype Learning for
generalized zero-shot open-set classification,
PR(147), 2024, pp. 110133.
Elsevier DOI
2312
Generalized zero-shot open-set classification,
Feature generation, Open-set prototype learning, Inter-class dispersion loss
BibRef
Liu, Z.[Zhe],
Li, Y.[Yun],
Yao, L.[Lina],
Chang, X.J.[Xiao-Jun],
Fang, W.[Wei],
Wu, X.J.[Xiao-Jun],
El Saddik, A.[Abdulmotaleb],
Simple Primitives With Feasibility- and Contextuality-Dependence for
Open-World Compositional Zero-Shot Learning,
PAMI(46), No. 1, January 2024, pp. 543-560.
IEEE DOI
2312
BibRef
Mancini, M.[Massimiliano],
Naeem, M.F.[Muhammad Ferjad],
Xian, Y.Q.[Yong-Qin],
Akata, Z.[Zeynep],
Learning Graph Embeddings for Open World Compositional Zero-Shot
Learning,
PAMI(46), No. 3, March 2024, pp. 1545-1560.
IEEE DOI
2402
Visualization, Training, Standards, Task analysis, Dogs,
Convolutional neural networks, Smoothing methods,
scene understanding
BibRef
Karthik, S.[Shyamgopal],
Mancini, M.[Massimiliano],
Akata, Z.[Zeynep],
KG-SP: Knowledge Guided Simple Primitives for Open World
Compositional Zero-Shot Learning,
CVPR22(9326-9335)
IEEE DOI
2210
Training, Visualization, Image recognition, Computational modeling,
Knowledge based systems, Semisupervised learning,
Representation learning
BibRef
Khan, M.G.Z.A.[Muhammad Gul Zain Ali],
Naeem, M.F.[Muhammad Ferjad],
Van Gool, L.J.[Luc J.],
Pagani, A.,
Stricker, D.[Didier],
Afzal, M.Z.[Muhammad Zeshan],
Learning Attention Propagation for Compositional Zero-Shot Learning,
WACV23(3817-3826)
IEEE DOI
2302
Training, Visualization, Buildings, Dogs, Bicycles, Benchmark testing,
Algorithms: Image recognition and understanding (object detection,
and un-supervised learning)
BibRef
Naeem, M.F.[Muhammad Ferjad],
Örnek, E.P.[Evin Pinar],
Xian, Y.Q.[Yong-Qin],
Van Gool, L.J.[Luc J.],
Tombari, F.[Federico],
3D Compositional Zero-Shot Learning with DeCompositional Consensus,
ECCV22(XXVIII:713-730).
Springer DOI
2211
BibRef
Naeem, M.F.[Muhammad Ferjad],
Xian, Y.Q.[Yong-Qin],
Tombari, F.[Federico],
Akata, Z.[Zeynep],
Learning Graph Embeddings for Compositional Zero-shot Learning,
CVPR21(953-962)
IEEE DOI
2111
Training, Visualization, Knowledge based systems, Semantics, Dogs,
Benchmark testing, Pattern recognition
BibRef
Mancini, M.[Massimiliano],
Naeem, M.F.[Muhammad Ferjad],
Xian, Y.Q.[Yong-Qin],
Akata, Z.[Zeynep],
Open World Compositional Zero-Shot Learning,
CVPR21(5218-5226)
IEEE DOI
2111
Training, Degradation, Visualization,
Computational modeling, Knowledge based systems, Benchmark testing
BibRef
Willes, J.[John],
Harrison, J.[James],
Harakeh, A.[Ali],
Finn, C.[Chelsea],
Pavone, M.[Marco],
Waslander, S.L.[Steven L.],
Bayesian Embeddings for Few-Shot Open World Recognition,
PAMI(46), No. 3, March 2024, pp. 1513-1529.
IEEE DOI
2402
Training, Bayes methods, Measurement, Taxonomy, Predictive models,
Pattern recognition, Optimization, Machine learning,
open-world learning
BibRef
Yang, F.L.[Feng-Lei],
Li, B.[Baomin],
Han, J.L.[Jing-Ling],
iCausalOSR: invertible Causal Disentanglement for Open-Set
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PR(149), 2024, pp. 110243.
Elsevier DOI
2403
Open-set recognition, Invertible model, Causal priors
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Li, T.Q.[Tian-Qi],
Pang, G.S.[Guan-Song],
Bai, X.[Xiao],
Zheng, J.[Jin],
Zhou, L.[Lei],
Ning, X.[Xin],
Learning adversarial semantic embeddings for zero-shot recognition in
open worlds,
PR(149), 2024, pp. 110258.
Elsevier DOI Code:
WWW Link.
2403
Zero-Shot Learning (ZSL), Open-Set Recognition (OSR),
Zero-Shot Open-Set Recognition (ZS-OSR)
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Zhao, T.H.[Tian-Hao],
Lin, Y.T.[Yu-Tian],
Wu, Y.[Yu],
Du, B.[Bo],
Promote knowledge mining towards open-world semi-supervised learning,
PR(149), 2024, pp. 110259.
Elsevier DOI Code:
WWW Link.
2403
Open world semi-supervised learning, Representation learning,
Novel class discovery
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Weng, T.Y.[Ting-Yu],
Xiao, J.[Jun],
Pan, H.[Hao],
Jiang, H.Y.[Hai-Yong],
PartCom: Part Composition Learning for 3D Open-Set Recognition,
IJCV(132), No. 4, April 2024, pp. 1393-1416.
Springer DOI
2404
BibRef
Li, Y.[Yun],
Liu, Z.[Zhe],
Jha, S.[Saurav],
Yao, L.[Lina],
Distilled Reverse Attention Network for Open-world Compositional
Zero-Shot Learning,
ICCV23(1782-1791)
IEEE DOI
2401
BibRef
Zhong, J.[Jian],
Wu, S.[Si],
Wong, H.S.[Hau-San],
Unknown Class Feature Transformation for Open Set Domain Adaptation
Without Source Data,
ICIP23(405-409)
IEEE DOI
2312
BibRef
Kim, B.[Byeonggeun],
Lee, J.T.[Jun-Tae],
Shim, K.[Kyuhong],
Chang, S.[Simyung],
Task-Agnostic Open-Set Prototype for Few-Shot Open-Set Recognition,
ICIP23(31-35)
IEEE DOI
2312
BibRef
Wang, H.Y.[Hao-Yu],
Pang, G.S.[Guan-Song],
Wang, P.[Peng],
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Glocal Energy-based Learning for Few-Shot Open-Set Recognition,
CVPR23(7507-7516)
IEEE DOI
2309
BibRef
Kim, G.[Geeho],
Kang, J.[Junoh],
Han, B.H.[Bo-Hyung],
Open-Set Representation Learning through Combinatorial Embedding,
CVPR23(19744-19753)
IEEE DOI
2309
BibRef
Peng, S.Y.[Song-You],
Genova, K.[Kyle],
Jiang, C.[Chiyu],
Tagliasacchi, A.[Andrea],
Pollefeys, M.[Marc],
Funkhouser, T.[Thomas],
OpenScene: 3D Scene Understanding with Open Vocabularies,
CVPR23(815-824)
IEEE DOI
2309
BibRef
Deng-Xiong, X.[Xiwen],
Kong, Y.[Yu],
Ancestor Search: Generalized Open Set Recognition via Hyperbolic Side
Information Learning,
WACV23(3992-4001)
IEEE DOI
2302
Taxonomy, Search problems, Distortion, Indexes,
Algorithms: Image recognition and understanding (object detection, segmentation)
BibRef
Palechor, A.[Andres],
Bhoumik, A.[Annesha],
Günther, M.[Manuel],
Large-Scale Open-Set Classification Protocols for ImageNet,
WACV23(42-51)
IEEE DOI
2302
Training, Measurement, Protocols, Earth Observing System, Robustness,
Classification algorithms, Partitioning algorithms
BibRef
Pal, D.[Debabrata],
Bose, S.[Shirsha],
Banerjee, B.[Biplab],
Jeppu, Y.[Yogananda],
MORGAN: Meta-Learning-based Few-Shot Open-Set Recognition via
Generative Adversarial Network,
WACV23(6284-6293)
IEEE DOI
2302
Training, Measurement, Image recognition, Benchmark testing,
Generative adversarial networks, Feature extraction,
Remote Sensing
BibRef
Lyu, Z.[Zongyao],
Gutierrez, N.B.[Nolan B.],
Beksi, W.J.[William J.],
MetaMax: Improved Open-Set Deep Neural Networks via Weibull
Calibration,
Novelty23(439-443)
IEEE DOI
2302
Training, Deep learning, Conferences, Computational modeling,
Neural networks, Calibration
BibRef
Dionelis, N.[Nikolaos],
Tsaftaris, S.A.[Sotirios A.],
Yaghoobi, M.[Mehrdad],
CTR: Contrastive Training Recognition Classifier for Few-Shot
Open-World Recognition,
ICPR22(1792-1799)
IEEE DOI
2212
Training, Roads, Medical services, Benchmark testing, Thorax,
Robustness, Safety
BibRef
Joseph, K.J.,
Paul, S.[Sujoy],
Aggarwal, G.[Gaurav],
Biswas, S.[Soma],
Rai, P.[Piyush],
Han, K.[Kai],
Balasubramanian, V.N.[Vineeth N.],
Novel Class Discovery Without Forgetting,
ECCV22(XXIV:570-586).
Springer DOI
2211
BibRef
Roy, S.[Subhankar],
Liu, M.X.[Ming-Xuan],
Zhong, Z.[Zhun],
Sebe, N.[Nicu],
Ricci, E.[Elisa],
Class-Incremental Novel Class Discovery,
ECCV22(XXXIII:317-333).
Springer DOI
2211
BibRef
Zhou, X.Y.[Xing-Yi],
Girdhar, R.[Rohit],
Joulin, A.[Armand],
Krähenbühl, P.[Philipp],
Misra, I.[Ishan],
Detecting Twenty-Thousand Classes Using Image-Level Supervision,
ECCV22(IX:350-368).
Springer DOI
2211
BibRef
Moon, W.J.[Won-Jun],
Park, J.[Junho],
Seong, H.S.[Hyun Seok],
Cho, C.H.[Cheol-Ho],
Heo, J.P.[Jae-Pil],
Difficulty-Aware Simulator for Open Set Recognition,
ECCV22(XXV:365-381).
Springer DOI
2211
BibRef
Cho, W.W.[Won-Woo],
Choo, J.[Jaegul],
Towards Accurate Open-Set Recognition via Background-Class
Regularization,
ECCV22(XXV:658-674).
Springer DOI
2211
BibRef
Ning, K.P.[Kun-Peng],
Zhao, X.[Xun],
Li, Y.[Yu],
Huang, S.J.[Sheng-Jun],
Active Learning for Open-set Annotation,
CVPR22(41-49)
IEEE DOI
2210
Learning systems, Training, Costs, Annotations, Object detection,
Detectors, Machine learning
BibRef
Ahmad, T.[Touqeer],
Dhamija, A.R.[Akshay Raj],
Jafarzadeh, M.[Mohsen],
Cruz, S.[Steve],
Rabinowitz, R.[Ryan],
Li, C.C.[Chun-Chun],
Boult, T.E.[Terrance E.],
Variable Few Shot Class Incremental and Open World Learning,
CLVision22(3687-3698)
IEEE DOI
2210
BibRef
Earlier: A1, A2, A4, A5, A6, A3, A7:
Few-Shot Class Incremental Learning Leveraging Self-Supervised
Features,
L3D-IVU22(3899-3909)
IEEE DOI
2210
Representation learning, Codes, Benchmark testing,
Feature extraction, Power capacitors
Training, Self-supervised learning, Data models, Generators
BibRef
Marmoreo, F.[Federico],
Carrazco, J.I.D.[Julio Ivan Davila],
Cavazza, J.[Jacopo],
Murino, V.[Vittorio],
Towards Open Zero-Shot Learning,
CIAP22(II:564-575).
Springer DOI
2205
BibRef
Fontanel, D.[Dario],
Cermelli, F.[Fabio],
Geraci, A.[Antonino],
Musarra, M.[Mauro],
Tarantino, M.[Matteo],
Caputo, B.[Barbara],
Relaxing the Forget Constraints in Open World Recognition,
CIAP22(I:751-763).
Springer DOI
2205
BibRef
Wang, Y.Z.[Ye-Zhen],
Li, B.[Bo],
Che, T.[Tong],
Zhou, K.Y.[Kai-Yang],
Liu, Z.W.[Zi-Wei],
Li, D.S.[Dong-Sheng],
Energy-Based Open-World Uncertainty Modeling for Confidence
Calibration,
ICCV21(9282-9291)
IEEE DOI
2203
Deep learning, Uncertainty, Computational modeling,
Neural networks, Predictive models, Linear programming,
BibRef
Guo, Y.[Yunrui],
Camporese, G.[Guglielmo],
Yang, W.J.[Wen-Jing],
Sperduti, A.[Alessandro],
Ballan, L.[Lamberto],
Conditional Variational Capsule Network for Open Set Recognition,
ICCV21(103-111)
IEEE DOI
2203
Training, Image recognition, Computational modeling, Neurons,
Probabilistic logic, Feature extraction,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Farber, M.[Miriam],
Goldenberg, R.[Roman],
Leifman, G.[George],
Novich, G.[Gal],
Novel Ensemble Diversification Methods for Open-Set Scenarios,
WACV22(3361-3370)
IEEE DOI
2202
Training, Correlation, Image recognition,
Computational modeling, Diversity methods, Feature extraction,
Biometrics -> Face Processing
BibRef
Pal, D.[Debabrata],
Bundele, V.[Valay],
Sharma, R.[Renuka],
Banerjee, B.[Biplab],
Jeppu, Y.[Yogananda],
Few-Shot Open-Set Recognition of Hyperspectral Images with Outlier
Calibration Network,
WACV22(2091-2100)
IEEE DOI
2202
Training, Representation learning,
Image recognition, Manuals,
Semi- and Un- supervised Learning
BibRef
Lee, S.[Sanghyuk],
Lee, S.H.[Seung-Hyun],
Song, B.C.[Byung Cheol],
Contextual Gradient Scaling for Few-Shot Learning,
WACV22(3503-3512)
IEEE DOI
2202
Degradation, Adaptation models,
Computational modeling, Classification algorithms, Task analysis,
Deep Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Lee, J.[Jinsol],
AlRegib, G.[Ghassan],
Open-Set Recognition With Gradient-Based Representations,
ICIP21(469-473)
IEEE DOI
2201
Training, Image recognition, Neural networks, Detectors,
Predictive models, Task analysis, gradients, open-set recognition,
out-of-distribution
BibRef
Zhou, D.W.[Da-Wei],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
Learning Placeholders for Open-Set Recognition,
CVPR21(4399-4408)
IEEE DOI
2111
Training, Manifolds, Face recognition, MIMICs,
Transforms, Predictive models
BibRef
Zhong, Z.[Zhun],
Zhu, L.C.[Lin-Chao],
Luo, Z.M.[Zhi-Ming],
Li, S.Z.[Shao-Zi],
Yang, Y.[Yi],
Sebe, N.[Nicu],
OpenMix: Reviving Known Knowledge for Discovering Novel Visual
Categories in an Open World,
CVPR21(9457-9465)
IEEE DOI
2111
Training, Visualization, Computational modeling,
Benchmark testing, Data models, Pattern recognition
BibRef
Jeong, M.[Minki],
Choi, S.[Seokeon],
Kim, C.[Changick],
Few-shot Open-set Recognition by Transformation Consistency,
CVPR21(12561-12570)
IEEE DOI
2111
Learning systems, Adaptation models, Prototypes,
Estimation, Detectors, Pattern recognition
BibRef
Yue, Z.Q.[Zhong-Qi],
Wang, T.[Tan],
Sun, Q.[Qianru],
Hua, X.S.[Xian-Sheng],
Zhang, H.W.[Han-Wang],
Counterfactual Zero-Shot and Open-Set Visual Recognition,
CVPR21(15399-15409)
IEEE DOI
2111
Training, Visualization, Codes, Pattern recognition
BibRef
Jafarzadeh, M.[Mohsen],
Ahmad, T.[Touqeer],
Dhamija, A.R.[Akshay Raj],
Li, C.C.[Chun-Chun],
Cruz, S.[Steve],
Boult, T.E.[Terrance E.],
Automatic Open-World Reliability Assessment,
WACV21(1983-1992)
IEEE DOI
2106
Face recognition, Reliability theory,
Reliability engineering, Classification algorithms, Reliability
BibRef
Miller, D.[Dimity],
Sünderhauf, N.[Niko],
Milford, M.[Michael],
Dayoub, F.[Feras],
Class Anchor Clustering:
A Loss for Distance-based Open Set Recognition,
WACV21(3569-3577)
IEEE DOI
2106
Training, Protocols, Neural networks,
Training data, Benchmark testing
BibRef
Yu, Q.[Qing],
Ikami, D.[Daiki],
Irie, G.[Go],
Aizawa, K.[Kiyoharu],
Multi-task Curriculum Framework for Open-set Semi-supervised Learning,
ECCV20(XII: 438-454).
Springer DOI
2010
BibRef
Zhang, H.J.[Hong-Jie],
Li, A.[Ang],
Guo, J.[Jie],
Guo, Y.[Yanwen],
Hybrid Models for Open Set Recognition,
ECCV20(III:102-117).
Springer DOI
2012
detect samples not belonging to any of the classes in its training set.
BibRef
Bucci, S.[Silvia],
Loghmani, M.R.[Mohammad Reza],
Tommasi, T.[Tatiana],
On the Effectiveness of Image Rotation for Open Set Domain Adaptation,
ECCV20(XVI: 422-438).
Springer DOI
2010
BibRef
Bertinetto, L.[Luca],
Mueller, R.[Romain],
Tertikas, K.[Konstantinos],
Samangooei, S.[Sina],
Lord, N.A.[Nicholas A.],
Making Better Mistakes: Leveraging Class Hierarchies With Deep
Networks,
CVPR20(12503-12512)
IEEE DOI
2008
Standards, Measurement, Vegetation, Machine learning, Art,
Visualization, Pipelines
BibRef
Pan, Y.,
Yao, T.,
Li, Y.,
Ngo, C.,
Mei, T.,
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation,
CVPR20(13864-13872)
IEEE DOI
2008
Adaptation models, Data structures, Mutual information,
Data models, Training, Entropy, Task analysis
BibRef
Kundu, J.N.[Jogendra Nath],
Venkatesh, R.M.[Rahul Mysore],
Venkat, N.[Naveen],
Revanur, A.[Ambareesh],
Babu, R.V.[R. Venkatesh],
Class-incremental Domain Adaptation,
ECCV20(XIII:53-69).
Springer DOI
2011
BibRef
Feng, Q.,
Kang, G.,
Fan, H.,
Yang, Y.,
Attract or Distract: Exploit the Margin of Open Set,
ICCV19(7989-7998)
IEEE DOI
2004
data handling, decision theory, pattern classification, set theory,
domain adaptation, domain shift, semantic structure, open set data,
Benchmark testing
BibRef
Liu, H.[Hong],
Cao, Z.J.[Zhang-Jie],
Long, M.S.[Ming-Sheng],
Wang, J.M.[Jian-Min],
Yang, Q.A.[Qi-Ang],
Separate to Adapt:
Open Set Domain Adaptation via Progressive Separation,
CVPR19(2922-2931).
IEEE DOI
2002
BibRef
Fu, J.,
Wu, X.,
Zhang, S.,
Yan, J.,
Improved Open Set Domain Adaptation with Backpropagation,
ICIP19(2506-2510)
IEEE DOI
1910
Open set domain adaptation, Back propagation,
Symmetrical Kullback Leibler distance
BibRef
Saito, K.[Kuniaki],
Yamamoto, S.[Shohei],
Ushiku, Y.[Yoshitaka],
Harada, T.[Tatsuya],
Open Set Domain Adaptation by Backpropagation,
ECCV18(VI: 156-171).
Springer DOI
1810
BibRef
Yoshihashi, R.[Ryota],
Shao, W.[Wen],
Kawakami, R.[Rei],
You, S.[Shaodi],
Iida, M.[Makoto],
Naemura, T.[Takeshi],
Classification-Reconstruction Learning for Open-Set Recognition,
CVPR19(4011-4020).
IEEE DOI
2002
BibRef
Tan, S.H.[Shu-Han],
Jiao, J.[Jiening],
Zheng, W.S.[Wei-Shi],
Weakly Supervised Open-Set Domain Adaptation by Dual-Domain
Collaboration,
CVPR19(5389-5398).
IEEE DOI
2002
BibRef
Perera, P.[Pramuditha],
Morariu, V.I.[Vlad I.],
Jain, R.[Rajiv],
Manjunatha, V.[Varun],
Wigington, C.[Curtis],
Ordonez, V.[Vicente],
Patel, V.M.[Vishal M.],
Generative-Discriminative Feature Representations for Open-Set
Recognition,
CVPR20(11811-11820)
IEEE DOI
2008
Does the sample belong to one of the trained classes?
Training, Task analysis, Force, Image reconstruction, Shape, Semantics
BibRef
Oza, P.[Poojan],
Patel, V.M.[Vishal M.],
C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition,
CVPR19(2302-2311).
IEEE DOI
2002
BibRef
Sun, L.[Li],
Yu, X.Y.[Xiao-Yi],
Wang, L.[Liuan],
Sun, J.[Jun],
Inakoshi, H.[Hiroya],
Kobayashi, K.[Ken],
Kobashi, H.[Hiromichi],
Automatic Neural Network Search Method for Open Set Recognition,
ICIP19(4090-4094)
IEEE DOI
1910
Neural network search, open set, search space,
feature distribution, center loss
BibRef
Neal, L.[Lawrence],
Olson, M.[Matthew],
Fern, X.L.[Xiao-Li],
Wong, W.K.[Weng-Keen],
Li, F.X.[Fu-Xin],
Open Set Learning with Counterfactual Images,
ECCV18(VI: 620-635).
Springer DOI
1810
Label known plus detect unknown classes.
BibRef
Wang, Y.S.[Yi-Sen],
Liu, W.Y.[Wei-Yang],
Ma, X.J.[Xing-Jun],
Bailey, J.[James],
Zha, H.Y.[Hong-Yuan],
Song, L.[Le],
Xia, S.T.[Shu-Tao],
Iterative Learning with Open-set Noisy Labels,
CVPR18(8688-8696)
IEEE DOI
1812
Noise measurement, Feature extraction, Cats, Training, Training data,
Labeling, Convolutional neural networks
BibRef
Gao, H.[Hua],
Ekenel, H.K.[Hazim Kemal],
Stiefelhagen, R.[Rainer],
Robust Open-Set Face Recognition for Small-Scale Convenience
Applications,
DAGM10(393-402).
Springer DOI
1009
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Contrastive Learning .