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1609
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Earlier: A1, A2, A4, A3, A5:
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IEEE DOI
1403
BibRef
And: A1, A4, A2, A3, A5:
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Buildings.
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ICIP15(3545-3549)
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1512
Cross-modal retrieval
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1612
Hierarchical sparse representation
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And:
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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,
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Song, G.L.[Guo-Li],
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2102
Data models, Kernel, Correlation, Semantics, Gaussian processes,
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Convolutional neural networks, (CNNs)
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Deep Multimodal Fusion: A Hybrid Approach,
IJCV(126), No. 2-4, April 2018, pp. 440-456.
Springer DOI
1804
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Amer, M.R.[Mohamed R.],
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Multimodal fusion using dynamic hybrid models,
WACV14(556-563)
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1406
Computational modeling
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Liu, M.Z.[Ming-Zhe],
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1806
Multiple data source mining, Pattern analysis,
Data classification, Data clustering, Data fusion
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Shape-aware label fusion for multi-atlas frameworks,
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Elsevier DOI
1906
Multi-atlas label fusion, Shape models, Medical image segmentation
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Multiobject Fusion With Minimum Information Loss,
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IEEE DOI
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Generalized covariance intersection,
Kullback-Leibler divergence, random finite set, data fusion,
linear opinion pool
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Jiang, Z.Y.[Zhi-Ying],
Fan, X.[Xin],
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A Bilevel Integrated Model With Data-Driven Layer Ensemble for
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IEEE DOI
2012
Image fusion, Task analysis, Transforms, Optimization,
Magnetic resonance imaging, Dictionaries,
neural networks
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Xu, H.[Han],
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Jiang, J.J.[Jun-Jun],
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U2Fusion: A Unified Unsupervised Image Fusion Network,
PAMI(44), No. 1, January 2022, pp. 502-518.
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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],
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Cross-Modality Fusion and Progressive Integration Network for
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MultMed(24), 2022, pp. 2435-2448.
IEEE DOI
2205
Feature extraction, Fuses, Decoding,
Predictive models, Pipelines, Visualization, Stereoscopic 3D image,
Progressive integration
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Wang, J.P.[Jin-Ping],
Li, J.[Jun],
Shi, Y.L.[Yan-Li],
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AM³Net: Adaptive Mutual-Learning-Based Multimodal Data Fusion Network,
CirSysVideo(32), No. 8, August 2022, pp. 5411-5426.
IEEE DOI
2208
Feature extraction, Laser radar, Convolution, Kernel,
Data integration, Convolutional neural networks,
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Tu, H.W.[Huang-Wei],
Zhu, Y.[Yu],
Han, C.P.[Chang-Pei],
RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for
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Duan, J.L.[Jia-Li],
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Tran, S.[Son],
Yang, J.[Jinyu],
Xu, Y.[Yi],
Zeng, B.[Belinda],
Chilimbi, T.[Trishul],
Multi-modal Alignment using Representation Codebook,
CVPR22(15630-15639)
IEEE DOI
2210
Training, Representation learning, Image coding, Dictionaries,
Benchmark testing, Pattern recognition, Vision + language
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Xu, H.[Han],
Ma, J.Y.[Jia-Yi],
Yuan, J.[Jiteng],
Le, Z.L.[Zhu-Liang],
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RFNet: Unsupervised Network for Mutually Reinforcing Multi-modal
Image Registration and Fusion,
CVPR22(19647-19656)
IEEE DOI
2210
Measurement, Deformable models, Image registration,
Pattern recognition, Task analysis, Image fusion, Low-level vision
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Xue, Z.H.[Zi-Hui],
Ren, S.C.[Su-Cheng],
Gao, Z.Q.[Zheng-Qi],
Zhao, H.[Hang],
Multimodal Knowledge Expansion,
ICCV21(834-843)
IEEE DOI
2203
Multimodal sensors, Semisupervised learning, Data collection,
Data models, Internet, Task analysis, Vision + other modalities,
Transfer/Low-shot/Semi/Unsupervised Learning
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Zolfaghari, M.[Mohammadreza],
Zhu, Y.[Yi],
Gehler, P.[Peter],
Brox, T.[Thomas],
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video
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ICCV21(1430-1439)
IEEE DOI
2203
Vision + language, Vision + other modalities
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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,
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Shi, Z.S.[Zhen-Sheng],
Liang, J.[Ju],
Li, Q.Q.[Qian-Qian],
Zheng, H.Y.[Hai-Yong],
Gu, Z.R.[Zhao-Rui],
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.Y.[Li-Yue],
Lungren, M.P.[Matthew P.],
Yeung, S.[Serena],
GLoRIA: A Multimodal Global-Local Representation Learning Framework
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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.S.[Guo-Sheng],
Feng, L.[Lei],
Zhang, Y.[Yan],
Lv, F.M.[Feng-Mao],
Attention is not Enough: Mitigating the Distribution Discrepancy in
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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
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ICPR12(1084-1087).
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Farnebäck, G.[Gunnar],
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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 .