12.1.4.7 Fusion, General Multi-Modal

Chapter Contents (Back)
Fusion. Sensor Fusion. Multi-Modal Fusion. Cross-Modal Fusion.

Rogelj, P.[Peter], Kovacic, S.[Stanislav], Gee, J.C.[James C.],
Point similarity measures for non-rigid registration of multi-modal data,
CVIU(92), No. 1, October 2003, pp. 112-140.
Elsevier DOI 0310
BibRef

Hasan, M.[Mahmudul], Pickering, M.R.[Mark R.], Jia, X.P.[Xiu-Ping],
Robust Automatic Registration of Multimodal Satellite Images Using CCRE With Partial Volume Interpolation,
GeoRS(50), No. 10, October 2012, pp. 4050-4061.
IEEE DOI 1210
BibRef
Earlier:
Multi-modal Registration of SAR and Optical Satellite Images,
DICTA09(447-453).
IEEE DOI 0912
BibRef

Sutour, C.[Camille], Aujol, J.F.[Jean-François], Deledalle, C.A.[Charles-Alban], de Senneville, B.D.[Baudouin Denis],
Edge-Based Multi-modal Registration and Application for Night Vision Devices,
JMIV(53), No. 2, October 2015, pp. 131-150.
Springer DOI 1508
BibRef

Yu, J.G.[Jin-Gang], Gao, C.X.[Chang-Xin], Tian, J.W.[Jin-Wen],
Collaborative multicue fusion using the cross-diffusion process for salient object detection,
JOSA-A(33), No. 3, March 2016, pp. 404-415.
DOI Link 1603
Digital image processing BibRef

Pitts, B., Riggs, S.L., Sarter, N.,
Crossmodal Matching: A Critical but Neglected Step in Multimodal Research,
HMS(46), No. 3, June 2016, pp. 445-450.
IEEE DOI 1605
Equating perceived intensities of stimuli across two sensory modalities. BibRef

Wang, K.[Kaiye], He, R.[Ran], Wang, L.[Liang], Wang, W.[Wei], Tan, T.N.[Tie-Niu],
Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval,
PAMI(38), No. 10, October 2016, pp. 2010-2023.
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 Cross-Modal Retrieval,
ACPR13(236-240)
IEEE DOI 1408
Buildings. graph theory BibRef

Li, Q.[Qi], Sun, Z.A.[Zhen-An], He, R.[Ran], Tan, T.N.[Tie-Niu],
Joint Alignment and Clustering via Low-Rank Representation,
ACPR13(591-595)
IEEE DOI 1408
image representation BibRef

Wang, K.[Kaiye], Wang, W.[Wei], Wang, L.[Liang],
Learning unified sparse representations for multi-modal data,
ICIP15(3545-3549)
IEEE DOI 1512
Cross-modal retrieval BibRef

Zu, C.[Chen], Wang, Z.X.[Zheng-Xia], Zhang, D.Q.[Dao-Qiang], Liang, P.P.[Pei-Peng], Shi, Y.H.[Yong-Hong], Shen, D.G.[Ding-Gang], Wu, G.R.[Guo-Rong],
Robust multi-atlas label propagation by deep sparse representation,
PR(63), No. 1, 2017, pp. 511-517.
Elsevier DOI 1612
Hierarchical sparse representation BibRef

Song, G.L.[Guo-Li], Wang, S.H.[Shu-Hui], Huang, Q.M.[Qing-Ming], 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], Tian, Q.[Qi],
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models,
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 BibRef

Li, K.[Ke], Zou, C.Q.[Chang-Qing], Bu, S.H.[Shu-Hui], Liang, Y.[Yun], Zhang, J.[Jian], Gong, M.L.[Ming-Lun],
Multi-modal feature fusion for geographic image annotation,
PR(73), No. 1, 2018, pp. 1-14.
Elsevier DOI 1709
Convolutional neural networks, (CNNs) BibRef

Amer, M.R.[Mohamed R.], Shields, T.[Timothy], Siddiquie, B.[Behjat], Tamrakar, A.[Amir], Divakaran, A.[Ajay], Chai, S.[Sek],
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 BibRef

Wang, R.[Ruili], Ji, W.T.[Wan-Ting], Liu, M.Z.[Ming-Zhe], Wang, X.[Xun], Weng, J.[Jian], Deng, S.[Song], 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 BibRef

Alvén, J.[Jennifer], Kahl, F.[Fredrik], Landgren, M.[Matilda], Larsson, V.[Viktor], Ulén, J.[Johannes], 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 BibRef

Gao, L.[Lin], 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 BibRef

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

Wang, J.P.[Jin-Ping], Li, J.[Jun], Shi, Y.L.[Yan-Li], Lai, J.H.[Jian-Huang], Tan, X.J.[Xiao-Jun],
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, and multimodal data classification BibRef

Tu, H.W.[Huang-Wei], Zhu, Y.[Yu], Han, C.P.[Chang-Pei],
RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Xu, H.[Han], Yuan, J.[Jiteng], Ma, J.Y.[Jia-Yi],
MURF: Mutually Reinforcing Multi-Modal Image Registration and Fusion,
PAMI(45), No. 10, October 2023, pp. 12148-12166.
IEEE DOI 2310
BibRef

Xu, H.[Han], Ma, J.Y.[Jia-Yi], Yuan, J.[Jiteng], Le, Z.L.[Zhu-Liang], Liu, W.[Wei],
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 BibRef

Li, J.Y.[Jia-Yuan], Hu, Q.W.[Qing-Wu], Zhang, Y.J.[Yong-Jun],
Multimodal Image Matching: A Scale-Invariant Algorithm and an Open Dataset,
PandRS(204), 2023, pp. 77-88.
Elsevier DOI 2310
Image matching, Feature descriptor, Dataset, SAR-optical, Multimodal images BibRef

Zhou, Y.[Yang], Han, Z.[Zhen], Dou, Z.[Zeng], Huang, C.B.[Cheng-Bin], Cong, L.[Li], Lv, N.[Ning], Chen, C.[Chen],
Edge Consistency Feature Extraction Method for Multi-Source Image Registration,
RS(15), No. 20, 2023, pp. 5051.
DOI Link 2310
BibRef

Lu, H.J.[Heng-Jie], Xu, S.G.[Shu-Gong], Wang, J.H.[Jia-Hao],
Multi-dataset fusion for multi-task learning on face attribute recognition,
PRL(173), 2023, pp. 72-78.
Elsevier DOI 2310
Face attribute recognition, Multi-dataset fusion, Multi-task learning, Knowledge distillation, Deep learning BibRef

Wei, J.[Jiwei], Yang, Y.[Yang], Xu, X.[Xing], Song, J.K.[Jing-Kuan], Wang, G.Q.[Guo-Qing], Shen, H.T.[Heng Tao],
Less is Better: Exponential Loss for Cross-Modal Matching,
CirSysVideo(33), No. 9, September 2023, pp. 5271-5280.
IEEE DOI 2310
BibRef

Islam, M.M.[Md Mofijul], Yasar, M.S.[Mohammad Samin], Iqbal, T.[Tariq],
MAVEN: A Memory Augmented Recurrent Approach for Multimodal Fusion,
MultMed(25), 2023, pp. 3694-3708.
IEEE DOI 2310
BibRef

Wang, Q.[Qun], Yang, B.[Boli], Li, L.[Luchun], Liang, H.Y.[Hong-Yi], Zhu, X.L.[Xiao-Lin], Cao, R.[Ruyin],
Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data,
RS(15), No. 20, 2023, pp. 5043.
DOI Link 2310
BibRef

Pang, H.X.[Hua-Xin], Wei, S.[Shikui], Zhang, G.[Gangjian], Zhang, S.Y.[Shi-Yin], Qiu, S.[Shuang], Zhao, Y.[Yao],
Heterogeneous Feature Alignment and Fusion in Cross-Modal Augmented Space for Composed Image Retrieval,
MultMed(25), 2023, pp. 6446-6457.
IEEE DOI 2311
BibRef

Zhang, J.[Jun], Jiao, L.C.[Li-Cheng], Ma, W.P.[Wen-Ping], Liu, F.[Fang], Liu, X.[Xu], Li, L.L.[Ling-Ling], Chen, P.[Puhua], Yang, S.Y.[Shu-Yuan],
Transformer Based Conditional GAN for Multimodal Image Fusion,
MultMed(25), 2023, pp. 8988-9001.
IEEE DOI 2312
BibRef

Wang, J.P.[Jin-Ping], Tan, X.J.[Xiao-Jun],
Mutually Beneficial Transformer for Multimodal Data Fusion,
CirSysVideo(33), No. 12, December 2023, pp. 7466-7479.
IEEE DOI 2312
BibRef

Luo, X.[Xing], Fu, G.Z.[Gui-Zhong], Yang, J.X.[Jiang-Xin], Cao, Y.L.[Yan-Long], Cao, Y.P.[Yan-Peng],
Multi-Modal Image Fusion via Deep Laplacian Pyramid Hybrid Network,
CirSysVideo(33), No. 12, December 2023, pp. 7354-7369.
IEEE DOI Code:
WWW Link. 2312
BibRef

Yan, X.[Xiaohu], Cao, Y.H.[Yi-Hang], Yang, Y.J.[Yi-Jun], Yao, Y.X.[Yong-Xiang],
Multi-Modal Image Registration Based on Phase Exponent Differences of the Gaussian Pyramid,
RS(15), No. 24, 2023, pp. 5764.
DOI Link 2401
BibRef

Chen, R.[Rui], Zhao, L.[Long],
Two-Level Integrity-Monitoring Method for Multi-Source Information Fusion Navigation,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Li, J.Y.[Jia-Yao], Li, L.[Li], Sun, R.Z.[Rui-Zhi], Yuan, G.[Gang], Wang, S.[Shufan], Sun, S.[Shulin],
MMAN-M2: Multiple multi-head attentions network based on encoder with missing modalities,
PRL(177), 2024, pp. 110-120.
Elsevier DOI 2401
Multi-modal fusion, Multi-head attention, Random missing modalities, Encoder-decoder, Missing modalities BibRef

Zhao, Z.X.[Zi-Xiang], Bai, H.[Haowen], Zhu, Y.Z.[Yuan-Zhi], Zhang, J.S.[Jiang-She], Xu, S.[Shuang], Zhang, Y.[Yulun], Zhang, K.[Kai], Meng, D.Y.[De-Yu], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion,
ICCV23(8048-8059)
IEEE DOI Code:
WWW Link. 2401
BibRef

Sun, Y.[Yuli], Lei, L.[Lin], Li, Z.[Zhang], Kuang, G.Y.[Gang-Yao],
Similarity and dissimilarity relationships based graphs for multimodal change detection,
PandRS(208), 2024, pp. 70-88.
Elsevier DOI Code:
WWW Link. 2402
Multimodal change detection, Dissimilarity relationship, k-farthest neighbors, k-nearest neighbors, Image regression BibRef

Zhao, Y.Y.[Yang-Yang], Zheng, Q.[Qingchun], Zhu, P.H.[Pei-Hao], Zhang, X.[Xu], Ma, W.P.[Wen-Peng],
TUFusion: A Transformer-Based Universal Fusion Algorithm for Multimodal Images,
CirSysVideo(34), No. 3, March 2024, pp. 1712-1725.
IEEE DOI Code:
WWW Link. 2403
Image fusion, Transformers, Feature extraction, Biomedical imaging, Deep learning, Heuristic algorithms, Visualization, fusion strategy BibRef

Moreshet, A.[Aviad], Keller, Y.[Yosi],
Attention-based multimodal image matching,
CVIU(241), 2024, pp. 103949.
Elsevier DOI 2403
Multisensor image matching, Deep learning, Attention-based BibRef


Han, K.Y.[Kai-Yang], Cao, F.[Fanzhi], Shi, T.X.[Tian-Xin], Wang, P.[Pu],
A Dual Attention Network for Multimodal Remote Sensing Image Matching,
CVIDL23(128-134)
IEEE DOI 2403
Training, Deep learning, Image matching, Nonlinear distortion, Imaging, Sensors, multimodal image matching, attention mechanism BibRef

Liu, B.[Bing], Xu, Z.Q.[Zi-Qi], Bao, X.L.[Xue-Liang], Zhong, Z.[Zhaohao],
MUNformer: A strong encoder that uses multi-level features extracted by different feature extractors for fusion,
CVIDL23(291-295)
IEEE DOI 2403
Semantics, Computer architecture, Feature extraction, Transformers, Decoding, Data mining, component, semantic segmentation BibRef

He, C.M.[Chun-Ming], Li, K.[Kai], Xu, G.X.[Guo-Xia], Zhang, Y.[Yulun], Hu, R.[Runze], Guo, Z.H.[Zhen-Hua], Li, X.[Xiu],
Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion,
ICCV23(12577-12587)
IEEE DOI 2401
BibRef

Liu, J.Y.[Jin-Yuan], Liu, Z.[Zhu], Wu, G.Y.[Guan-Yao], Ma, L.[Long], Liu, R.S.[Ri-Sheng], Zhong, W.[Wei], Luo, Z.X.[Zhong-Xuan], Fan, X.[Xin],
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation,
ICCV23(8081-8090)
IEEE DOI Code:
WWW Link. 2401
BibRef

Sippel, F.[Frank], Seiler, J.[Jürgen], Kaup, A.[André],
Cross Spectral Image Reconstruction Using a Deep Guided Neural Network,
ICIP23(226-230)
IEEE DOI 2312
BibRef

Myers, A.[Audun], Kvinge, H.[Henry], Emerson, T.[Tegan],
TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal Data,
TAG-PRA23(600-609)
IEEE DOI 2309
BibRef

Xue, Z.[Zihui], Marculescu, R.[Radu],
Dynamic Multimodal Fusion,
MULA23(2575-2584)
IEEE DOI 2309
BibRef

Li, X.[Xin], Ma, T.[Tao], Hou, Y.N.[Yue-Nan], Shi, B.[Botian], Yang, Y.C.[Yu-Chen], Liu, Y.[Youquan], Wu, X.J.[Xing-Jiao], Chen, Q.[Qin], Li, Y.[Yikang], Qiao, Y.[Yu], He, L.[Liang],
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion,
CVPR23(17524-17534)
IEEE DOI 2309
BibRef

Kong, L.K.[Ling-Ke], Qi, X.S.[X. Sharon], Shen, Q.J.[Qi-Jin], Wang, J.C.[Jia-Cheng], Zhang, J.Y.[Jing-Yi], Hu, Y.[Yanle], Zhou, Q.C.[Qi-Chao],
Indescribable Multi-Modal Spatial Evaluator,
CVPR23(9853-9862)
IEEE DOI 2309

WWW Link. BibRef

Zhao, Z.X.[Zi-Xiang], Bai, H.[Haowen], Zhang, J.S.[Jiang-She], Zhang, Y.[Yulun], Xu, S.[Shuang], Lin, Z.[Zudi], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion,
CVPR23(5906-5916)
IEEE DOI 2309
BibRef

Li, Y.[Yaowei], Quan, R.J.[Rui-Jie], Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Efficient Multimodal Fusion via Interactive Prompting,
CVPR23(2604-2613)
IEEE DOI 2309
BibRef

Wetzer, E.[Elisabeth], Lindblad, J.[Joakim], Sladoje, N.[Nataša],
Can Representation Learning for Multimodal Image Registration be Improved by Supervision of Intermediate Layers?,
IbPRIA23(261-275).
Springer DOI 2307
BibRef

Duan, J.L.[Jia-Li], Chen, L.Q.[Li-Qun], 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 BibRef

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 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.S.[Rogerio S.],
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.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 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.S.[Rogerio S.], 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 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.N.[Qing-Nan], Zhang, S.H.[Shang-Hang], 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 .


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