14.2.10 Open Set, Open World Recongnition

Chapter Contents (Back)
Recognition. Object Recognition. Open Set. Open World. Open Set Recognition. One issue is how to deal with all the out of distribution objects. Does the sample belong to training classes?

Zhang, H.[He], Patel, V.M.[Vishal M.],
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], Raveaux, R.[Romain], Tabbone, S.[Salvatore], da Silva Torres, R.[Ricardo],
Learning cost function for graph classification with open-set methods,
PRL(128), 2019, pp. 8-15.
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) BibRef

Geng, C.X.[Chuan-Xing], Tao, L.[Lue], Chen, S.C.[Song-Can],
Guided CNN for generalized zero-shot and open-set recognition using visual and semantic prototypes,
PR(102), 2020, pp. 107263.
Elsevier DOI 2003
Convolutional prototype learning, Generalized zero-shot Learning, Open set recognition BibRef

Othman, E., Bazi, Y.[Yakoub], Melgani, F., Alhichri, H.[Haikel], Alajlan, N.[Naif], Zuair, M.,
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], Alhichri, H.[Haikel], Alajlan, N.[Naif],
Deep Open-Set Domain Adaptation for Cross-Scene Classification based on Adversarial Learning and Pareto Ranking,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Loghmani, M.R.[Mohammad Reza], Vincze, M.[Markus], Tommasi, T.[Tatiana],
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 BibRef

Liu, S.J.[Sheng-Jie], Shi, Q.[Qian], Zhang, L.P.[Liang-Pei],
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning,
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], Ozturk, G.[Gurkan],
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 BibRef

Shermin, T.[Tasfia], Lu, G.J.[Guo-Jun], Teng, S.W.[Shyh Wei], Murshed, M.[Manzur], Sohel, F.[Ferdous],
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 BibRef

Chambers, L.[Lorraine], Gaber, M.M.[Mohamed Medhat],
DeepStreamOS: Fast open-Set classification for convolutional neural networks,
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 network,
PR(128), 2022, pp. 108657.
Elsevier DOI 2205
Open domain recognition, Image classification, Adaptive openness, Prototype learning, Unknown class recognition BibRef

Bevandic, P.[Petra], Krešo, I.[Ivan], Oršic, M.[Marin], Šegvic, S.[Siniša],
Dense open-set recognition based on training with noisy negative images,
IVC(124), 2022, pp. 104490.
Elsevier DOI 2208
Dense prediction, Semantic segmentation, Dense open-set recognition, Outlier detection BibRef

Giusti, E.[Elisa], Ghio, S.[Selenia], Oveis, A.H.[Amir Hosein], Martorella, M.[Marco],
Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Chen, G.Y.[Guang-Yao], Peng, P.X.[Pei-Xi], Wang, X.Q.[Xiang-Qian], 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 BibRef

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], Reid, I.D.[Ian D.], Carneiro, G.[Gustavo],
LongReMix: Robust learning with high confidence samples in a noisy label environment,
PR(133), 2023, pp. 109013.
Elsevier DOI 2210
BibRef
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 BibRef

Naranjo-Alcazar, J.[Javier], Perez-Castanos, S.[Sergi], Zuccarello, P.[Pedro], Torres, A.M.[Ana M.], Lopez, J.J.[Jose J.], Ferri, F.J.[Francesc J.], Cobos, M.[Maximo],
An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments,
PRL(164), 2022, pp. 40-45.
Elsevier DOI 2212
Audio Dataset, Classification, Few-Shot Learning, Machine Listening, Open-set Recognition, Sound Processing BibRef

Sun, J.Y.[Jia-Yin], Wang, H.[Hong], Dong, Q.[Qiulei],
MoEP-AE: Autoencoding Mixtures of Exponential Power Distributions for Open-Set Recognition,
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 BibRef

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],
Spatial location constraint prototype loss for open set recognition,
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],
From anomaly detection to open set recognition: Bridging the gap,
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 Classification,
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 Recognition,
PR(149), 2024, pp. 110243.
Elsevier DOI 2403
Open-set recognition, Invertible model, Causal priors BibRef

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) BibRef

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 BibRef

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


Hu, H.[Hexiang], Luan, Y.[Yi], Chen, Y.[Yang], Khandelwal, U.[Urvashi], Joshi, M.[Mandar], Lee, K.[Kenton], Toutanova, K.[Kristina], Chang, M.W.[Ming-Wei],
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities,
ICCV23(12031-12041)
IEEE DOI 2401
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 .


Last update:Apr 10, 2024 at 09:54:40