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0310
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1210
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Multi-modal Registration of SAR and Optical Satellite Images,
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Digital image processing
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1605
Equating perceived intensities of stimuli across two sensory modalities.
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IEEE DOI
1609
BibRef
Earlier: A1, A2, A4, A3, A5:
Learning Coupled Feature Spaces for Cross-Modal Matching,
ICCV13(2088-2095)
IEEE DOI
1403
BibRef
And: A1, A4, A2, A3, A5:
Multi-modal Subspace Learning with Joint Graph Regularization for
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ACPR13(236-240)
IEEE DOI
1408
Buildings.
graph theory
BibRef
Li, Q.[Qi],
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Joint Alignment and Clustering via Low-Rank Representation,
ACPR13(591-595)
IEEE DOI
1408
image representation
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Learning unified sparse representations for multi-modal data,
ICIP15(3545-3549)
IEEE DOI
1512
Cross-modal retrieval
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Zu, C.[Chen],
Wang, Z.X.[Zheng-Xia],
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Elsevier DOI
1612
Hierarchical sparse representation
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Song, G.L.[Guo-Li],
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Tian, Q.[Qi],
Multimodal Similarity Gaussian Process Latent Variable Model,
IP(26), No. 9, September 2017, pp. 4168-4181.
IEEE DOI
1708
BibRef
And:
Multimodal Gaussian Process Latent Variable Models with Harmonization,
ICCV17(5039-5047)
IEEE DOI
1802
BibRef
Earlier:
Similarity Gaussian Process Latent Variable Model for Multi-modal
Data Analysis,
ICCV15(4050-4058)
IEEE DOI
1602
Gaussian processes, content-based retrieval, gradient methods,
learning (artificial intelligence), pattern classification,
cross-modal content retrieval, distance preservation,
gradient descent techniques, heterogeneous modalities,
BibRef
Song, G.L.[Guo-Li],
Wang, S.H.[Shu-Hui],
Huang, Q.M.[Qing-Ming],
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Harmonized Multimodal Learning with Gaussian Process Latent Variable
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PAMI(43), No. 3, March 2021, pp. 858-872.
IEEE DOI
2102
Data models, Kernel, Correlation, Semantics, Gaussian processes,
Learning systems, Probabilistic logic, Multimodal learning,
cross-modal retrieval
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Li, K.[Ke],
Zou, C.Q.[Chang-Qing],
Bu, S.[Shuhui],
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Multi-modal feature fusion for geographic image annotation,
PR(73), No. 1, 2018, pp. 1-14.
Elsevier DOI
1709
Convolutional neural networks, (CNNs)
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Amer, M.R.[Mohamed R.],
Shields, T.[Timothy],
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Tamrakar, A.[Amir],
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Deep Multimodal Fusion: A Hybrid Approach,
IJCV(126), No. 2-4, April 2018, pp. 440-456.
Springer DOI
1804
BibRef
Amer, M.R.[Mohamed R.],
Siddiquie, B.[Behjat],
Khan, S.[Saad],
Divakaran, A.[Ajay],
Sawhney, H.S.[Harpreet S.],
Multimodal fusion using dynamic hybrid models,
WACV14(556-563)
IEEE DOI
1406
Computational modeling
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Wang, R.[Ruili],
Ji, W.T.[Wan-Ting],
Liu, M.Z.[Ming-Zhe],
Wang, X.[Xun],
Weng, J.[Jian],
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Gao, S.Y.[Su-Ying],
Yuan, C.A.[Chang-An],
Review on mining data from multiple data sources,
PRL(109), 2018, pp. 120-128.
Elsevier DOI
1806
Multiple data source mining, Pattern analysis,
Data classification, Data clustering, Data fusion
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Alvén, J.[Jennifer],
Kahl, F.[Fredrik],
Landgren, M.[Matilda],
Larsson, V.[Viktor],
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Enqvist, O.[Olof],
Shape-aware label fusion for multi-atlas frameworks,
PRL(124), 2019, pp. 109-117.
Elsevier DOI
1906
Multi-atlas label fusion, Shape models, Medical image segmentation
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Battistelli, G.[Giorgio],
Chisci, L.[Luigi],
Multiobject Fusion With Minimum Information Loss,
SPLetters(27), 2020, pp. 201-205.
IEEE DOI
2002
Generalized covariance intersection,
Kullback-Leibler divergence, random finite set, data fusion,
linear opinion pool
BibRef
Liu, R.S.[Ri-Sheng],
Liu, J.Y.[Jin-Yuan],
Jiang, Z.Y.[Zhi-Ying],
Fan, X.[Xin],
Luo, Z.X.[Zhong-Xuan],
A Bilevel Integrated Model With Data-Driven Layer Ensemble for
Multi-Modality Image Fusion,
IP(30), 2021, pp. 1261-1274.
IEEE DOI
2012
Image fusion, Task analysis, Transforms, Optimization,
Magnetic resonance imaging, Dictionaries,
neural networks
BibRef
Xu, H.[Han],
Ma, J.Y.[Jia-Yi],
Jiang, J.J.[Jun-Jun],
Guo, X.J.[Xiao-Jie],
Ling, H.B.[Hai-Bin],
U2Fusion: A Unified Unsupervised Image Fusion Network,
PAMI(44), No. 1, January 2022, pp. 502-518.
IEEE DOI
2112
Image fusion, Task analysis, Feature extraction, Measurement,
Supervised learning, Data mining, Training, Image fusion,
continual learning
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Mao, Y.D.[Yu-Dong],
Jiang, Q.P.[Qiu-Ping],
Cong, R.M.[Run-Min],
Gao, W.[Wei],
Shao, F.[Feng],
Kwong, S.[Sam],
Cross-Modality Fusion and Progressive Integration Network for
Saliency Prediction on Stereoscopic 3D Images,
MultMed(24), 2022, pp. 2435-2448.
IEEE DOI
2205
Feature extraction, Fuses, Decoding,
Predictive models, Pipelines, Visualization, Stereoscopic 3D image,
Progressive integration
BibRef
Zolfaghari, M.[Mohammadreza],
Zhu, Y.[Yi],
Gehler, P.[Peter],
Brox, T.[Thomas],
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video
Representations,
ICCV21(1430-1439)
IEEE DOI
2203
Vision + language, Vision + other modalities
BibRef
Panda, R.[Rameswar],
Chen, C.F.R.[Chun-Fu Richard],
Fan, Q.F.[Quan-Fu],
Sun, X.[Ximeng],
Saenko, K.[Kate],
Oliva, A.[Aude],
Feris, R.[Rogerio],
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition,
ICCV21(7556-7565)
IEEE DOI
2203
Adaptation models, Computational modeling, Standards,
Video analysis and understanding,
BibRef
Shi, Z.S.[Zhen-Sheng],
Liang, J.[Ju],
Li, Q.Q.[Qian-Qian],
Zheng, H.Y.[Hai-Yong],
Gu, Z.[Zhaorui],
Dong, J.Y.[Jun-Yu],
Zheng, B.[Bing],
Multi-Modal Multi-Action Video Recognition,
ICCV21(13658-13667)
IEEE DOI
2203
Convolutional codes, Visualization, Analytical models,
Computational modeling, Benchmark testing,
Video analysis and understanding
BibRef
Huang, S.C.[Shih-Cheng],
Shen, L.[Liyue],
Lungren, M.P.[Matthew P.],
Yeung, S.[Serena],
GLoRIA: A Multimodal Global-Local Representation Learning Framework
for Label-efficient Medical Image Recognition,
ICCV21(3922-3931)
IEEE DOI
2203
Representation learning, Deep learning, Training,
Image segmentation, Image recognition, Image analysis,
Vision + language
BibRef
Chen, B.[Brian],
Rouditchenko, A.[Andrew],
Duarte, K.[Kevin],
Kuehne, H.[Hilde],
Thomas, S.[Samuel],
Boggust, A.[Angie],
Panda, R.[Rameswar],
Kingsbury, B.[Brian],
Feris, R.[Rogerio],
Harwath, D.[David],
Glass, J.[James],
Picheny, M.[Michael],
Chang, S.F.[Shih-Fu],
Multimodal Clustering Networks for Self-supervised Learning from
Unlabeled Videos,
ICCV21(7992-8001)
IEEE DOI
2203
Training, Optical losses, Location awareness, Annotations, Semantics,
Pipelines, Video analysis and understanding,
Vision + other modalities
BibRef
Liang, T.[Tao],
Lin, G.[Guosheng],
Feng, L.[Lei],
Zhang, Y.[Yan],
Lv, F.[Fengmao],
Attention is not Enough: Mitigating the Distribution Discrepancy in
Asynchronous Multimodal Sequence Fusion,
ICCV21(8128-8136)
IEEE DOI
2203
Correlation, Fuses, Computational modeling, Benchmark testing,
Transformers, Acoustics, Video analysis and understanding,
BibRef
Liu, Y.Z.[Yun-Ze],
Fan, Q.[Qingnan],
Zhang, S.[Shanghang],
Dong, H.[Hao],
Funkhouser, T.[Thomas],
Yi, L.[Li],
Contrastive Multimodal Fusion with TupleInfoNCE,
ICCV21(734-743)
IEEE DOI
2203
Training, Representation learning, Benchmark testing,
Task analysis, Optimization, Vision + other modalities, Representation learning
BibRef
Son, C.H.,
Zhang, X.P.,
Multimodal fusion via a series of transfers for noise removal,
ICIP17(530-534)
IEEE DOI
1803
Image representation, Imaging,
Pattern recognition, Visual communication,
Near-infrared imaging, multimodal fusion
BibRef
Shrivastava, A.[Ashish],
Rastegari, M.[Mohammad],
Shekhar, S.[Sumit],
Chellappa, R.[Rama],
Davis, L.S.[Larry S.],
Class consistent multi-modal fusion with binary features,
CVPR15(2282-2291)
IEEE DOI
1510
BibRef
Kasiri, K.[Keyvan],
Fieguth, P.W.[Paul W.],
Clausi, D.A.[David A.],
Self-similarity measure for multi-modal image registration,
ICIP16(4498-4502)
IEEE DOI
1610
BibRef
Earlier:
Structural Representations for Multi-modal Image Registration Based on
Modified Entropy,
ICIAR15(82-89).
Springer DOI
1507
Brain.
BibRef
Glodek, M.[Michael],
Schels, M.[Martin],
Palm, G.[Gunther],
Schwenker, F.[Friedhelm],
Multi-modal Fusion based on classifiers using reject options and Markov
Fusion Networks,
ICPR12(1084-1087).
WWW Link.
1302
BibRef
Forsberg, D.[Daniel],
Farnebäck, G.[Gunnar],
Knutsson, H.[Hans],
Westin, C.F.[Carl-Fredrik],
Multi-modal Image Registration Using Polynomial Expansion and Mutual
Information,
WBIR12(40-49).
Springer DOI
1208
BibRef
Town, C.[Christopher],
Zhu, Z.G.[Zhi-Gang],
Sensor Fusion and Environmental Modelling for Multimodal Sentient
Computing,
MSCSAS07(1-2).
IEEE DOI
0706
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Fusion, Range or Depth and Intensity or Color Data .