The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Optimal multimodal fusion for multimedia data analysis
Abstract
Considerable research has been devoted to utilizing multimodal features for better understanding multimedia data. However, two core research issues have not yet been adequately addressed. First, given a set of features extracted from multiple media sources (e.g., extracted from the visual, audio, and caption track of videos), how do we determine the best modalities? Second, once a set of modal-ities has been identified, how do we best fuse them to map to se-mantics? In this paper, we propose a two-step approach. The first step finds statistically independent modalities from raw features. In the second step, we use super-kernel fusion to determine the optimal combination of individual modalities. We carefully ana-lyze the tradeoffs between three design factors that affect fusion performance: modality independence, curse of dimensionality, and fusion-model complexity. Through analytical and empirical studies, we demonstrate that our two-step approach, which achieves a care-ful balance of the three design factors, can improve class-prediction accuracy over traditional techniques.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Attention-based fusion [49,50,51] aligns key features and dynamically assigns weights between modalities for deeper semantic capture.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [49]
Citations
Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Goodall, C. R., & Jolliffe, I. T. (1988) · Technometrics · doi:10.2307/1270093 · Cited by 8,797
Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8 · Cited by 8,481
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,834
A Fast Fixed-Point Algorithm for Independent Component Analysis
Hyvärinen, A., & Erkki Oja, A. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1483 · Cited by 3,373
Query by image and video content: the QBIC system
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petković, D., et al. (1995) · Computer · doi:10.1109/2.410146 · Cited by 3,097
Adaptive Control Processes: A Guided Tour
Bellman, R. E. (1961) · doi:10.1515/9781400874668 · Cited by 2,802
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Dietterich, T. G., & Bakiri, G. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.105 · Cited by 2,739
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403
A New Learning Algorithm for Blind Signal Separation
Амари, Ш.-И., Cichocki, A. S., & Yang, H. H. (1995) · Cited by 1,904
Research works printed but not linked: 10
- A. Amir , H. W, G. Iyengar , C.- Y. Lin , M. Naphade , A. Natsev , C. Neti , H. J. Nock , J. R. Smith , B. L. Tseng , Y. Wu , and D. Zhang . IBM research TRECVID-2003 system . NIST Text Retrieval Conf. (TREC) , 2003 .]] A. Amir, H. W, G. Iyengar, C.-Y.Lin, M. Naphade, A. Natsev, C. Neti, H. J. Nock, J. R. Smith, B. L. Tseng, Y. Wu, and D. Zhang. IBM research TRECVID-2003 system. NIST Text Retrieval Conf. (TREC), 2003.]]
- 10.5555/645503.656271 · DOI
- 10.5555/521772.791882 · DOI
- 10.5555/1622826.1622834 · DOI
- 10.5555/645496.658058 · DOI
- D. L. Donoho . High-dimensional data analysis: The curses and blessings of dimensionality . American Math. Society Lecture---Match Challenges of the 21st Century , 2000 .]] D. L. Donoho. High-dimensional data analysis: The curses and blessings of dimensionality. American Math. Society Lecture---Match Challenges of the 21st Century, 2000.]]
- Y. Rui , T. S. Huang , and S. F. Chang . Image retrieval: Past, present, and future . International Symposium on Multimedia Information Processing , 1997 .]] Y. Rui, T. S. Huang, and S. F. Chang. Image retrieval: Past, present, and future. International Symposium on Multimedia Information Processing, 1997.]]
- J. R. Smith and S. F. Chang . Automatic image retrieval using color and texture . IEEE Trans Pattern Anal Mach Intell , 1996 .]] J. R. Smith and S. F. Chang. Automatic image retrieval using color and texture. IEEE Trans Pattern Anal Mach Intell, 1996.]]
- 10.5555/1622859.1622868 · DOI
- 10.5555/1760167.1760197 · DOI
Cited by
Sorted by citations
Items 101–200 of 228
Combining Multi-modal Features for Social Media Analysis
Nikolopoulos, S., Giannakidou, E., Kompatsiaris, I. Y., Patras, I., & Vakali, A. I. (2011) · Springer eBooks · doi:10.1007/978-0-85729-436-4_4 · Cited by 11
Content-based search and browsing in semantic multimedia retrieval
Rautiainen, M. (2006) · Cited by 11
International Conference on Semantic Computing-Copyright
(2007) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2007.3 · Cited by 11
Evidential Reasoning for Multimodal Fusion in Human Computer Interaction
Reddy, B. S. (2007) · UWSpace (University of Waterloo) · Cited by 11
Multiple kernel visual-auditory representation learning for retrieval
Zhang, H., Zhang, W., Liu, W., Xu, X., & Fan, H. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3294-5 · Cited by 10
Xu, C., Hou, X., Liu, J., Li, C., Huang, T., Zhu, X., Niu, M. Y., Sun, L., Tang, P., Xu, T., et al. (2023) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc59245.2023.00014 · Cited by 10
A cross-media evolutionary timeline generation framework based on iterative recommendation
Xu, S., Kong, L., & Zhang, Y. (2013) · International Conference on Multimedia Retrieval · doi:10.1145/2461466.2461480 · Cited by 10
Luo, H.-Z., Fan, J.-P., Satoh, S., Yang, J., & Ribarsky, W. (2008) · Signal processing. Image communication · doi:10.1016/j.image.2008.04.014 · Cited by 13
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
Ha, H.-Y., Fleites, F. C., & Chen, S. (2013) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/jmdem.2013040103 · Cited by 9
SMG: A System-Level Modality Gating Facility for Fast and Energy-Efficient Multimodal Computing
Hou, X., Tang, P., Li, C., Liu, J., Xu, C., Cheng, K.-T. T., & Guo, M. (2023) · IEEE Real-Time Systems Symposium (RTSS) · doi:10.1109/rtss59052.2023.00033 · Cited by 9
Query-Document-Dependent Fusion: A Case Study of Multimodal Music Retrieval
Li, Z., Zhang, B., Yu, Y., Shen, J., & Wang, Y. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2280437 · Cited by 9
Audio-Visual Feature Fusion for Vehicles Classification in a Surveillance System
Wang, T., Zhu, Z., & Hammoud, R. I. (2013) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.64 · Cited by 9
Extracting Semantics from Multimedia Content: Challenges and Solutions
Xie, L., & Yan, R. (2008) · Signals and communication technology · doi:10.1007/978-0-387-76569-3_2 · Cited by 9
Sevillano, X., Socoró, J. C., & Álías, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.09.064 · Cited by 9
Input Dropout for Spatially Aligned Modalities
de Blois, S., Garon, M., Gagné, C., & Lalonde, J. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190858 · Cited by 9
Multi-graph embedding discriminative correlation feature learning for image recognition
Su, S., Ge, H., & Tong, Y. (2017) · Signal Processing Image Communication · doi:10.1016/j.image.2017.10.005 · Cited by 8
Joint intermodal and intramodal correlation preservation for semi-paired learning
Guo, X., Wang, S., Tie, Y., Qi, L., & Guan, L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.013 · Cited by 8
Artificial Intelligence in Biometrics: Uncovering Intricacies of Human Body and Mind
Gavrilova, M. L., Luchak, I., Sudhakar, T., & Tumpa, S. N. (2022) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-93052-3_7 · Cited by 8
Multi-Concept Multi-Modality Active Learning for Interactive Video Annotation
Wang, M., Hua, X., Song, Y., Tang, J., & Dai, L.-R. (2007) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2007.14 · Cited by 8
Yilmaz, T., Yazıcı, A., & Yıldırım, Y. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24764-4_14 · Cited by 7
Using the geographic scopes of web documents for contextual advertising
Anastácio, I., Martins, B., & Calado, P. P. (2010) · Workshop on Geographic Information Retrieval (GIR) · doi:10.1145/1722080.1722103 · Cited by 7
Multimedia Fusion With Mean-Covariance Analysis
Wang, X., & Kankanhalli, M. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2225027 · Cited by 7
Portfolio theory of multimedia fusion
Wang, X., & Kankanhalli, M. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874062 · Cited by 7
Automatic Image Annotation Based on Multi-Auxiliary Information
Zhang, P., Wei, Z., Li, Y., & Zhao, C. (2017) · IEEE Access · doi:10.1109/access.2017.2749252 · Cited by 7
Discriminative Fusion Approach for Automatic Image Annotation
Wang, D., Gao, S., Tian, Q., & Sung, W.-K. K. (2005) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2005.248595 · Cited by 7
Semantics reinforcement and fusion learning for multimedia streams
Joshi, D., Naphade, M., & Natsev, A. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282328 · Cited by 7
Harmonium Models for Video Classification
Yang, J. J., Yan, R., Liu, Y., & Xing, E. P. (2008) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.103 · Cited by 7
Multimedia multimodal methodologies
Guan, L., Muneesawang, P., Wang, Y., Zhang, R., Tie, Y., Bulzacki, A., & Ibrahim, M. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202824 · Cited by 6
Vision and natural language for metadata extraction from scientific PDF documents
Boukhers, Z., & Bouabdallah, A. (2022) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3529372.3533295 · Cited by 6
A novel framework for retrieval and interactive visualization of multimodal data
Kalamaras, I., Mademlis, A., Malassiotis, S., & Tzovaras, D. K. (2013) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.518 · Cited by 6
A Bayesian network modeling approach for cross media analysis
Lakka, C., Nikolopoulos, S., Varytimidis, C., & Kompatsiaris, I. Y. (2011) · Signal Processing Image Communication · doi:10.1016/j.image.2011.01.004 · Cited by 6
Filling the Semantic Gap in Video Retrieval: An Exploration
Hauptmann, A. G., Yan, R., Lin, W., Christel, M. G., & Wactlar, H. D. (2008) · Springer eBooks · doi:10.1007/978-1-84800-076-6_10 · Cited by 6
Nonnegative cross-media recoding of visual-auditory content for social media analysis
Zhang, H., & Xu, X. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-1970-x · Cited by 6
Probabilistic model supported rank aggregation for the semantic concept detection in video
Ding, D., & Zhang, B. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282364 · Cited by 6
Golugula, A., Lee, G., Master, S. R., Feldman, M. D., Tomaszewski, J. E., & Madabhushi, A. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091588 · Cited by 6
A Hybrid Approach to Improving Semantic Extraction of News Video
Hauptmann, A. G., Chen, M.-Y., Christel, M. G., Lin, W.-H., & Yang, J. (2007) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2007.68 · Cited by 6
Exploiting Concept Association to Boost Multimedia Semantic Concept Detection
Gao, S., Zhu, X., & Sun, Q. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366074 · Cited by 6
Topic models for semantics-preserving video compression
Wanke, J., Ulges, A., Lampert, C. H., & Breuel, T. M. (2010) · Multimedia Information Retrieval · doi:10.1145/1743384.1743433 · Cited by 5
Low-level multimodal integration on Riemannian manifolds for automatic pedestrian detection
Biagio, M. S., Crocco, M., Cristani, M., Martelli, S., & Murino, V. (2012) · Cited by 5
An Approach for Cross-media Retrieval with Cross-reference Graph and PageRank
Zhuang, Y., Shan, H., & Wu, F. Q. (2006) · International Multimedia Modelling Conference (MMM) · doi:10.1109/mmmc.2006.1651316 · Cited by 5
A Novel Data-Parallel Coprocessor for Multimedia Signal Processing
Mingche, L., Dai, K., Lu, H., & Wang, Z. (2006) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2006.262513 · Cited by 5
Design of a Configurable and Extensible Tcore Processor Based on Transport Triggered Architecture
Guo, W., Wei, J., Yao, Y., Shi, Z., & Su, W. (2009) · doi:10.1109/csie.2009.233 · Cited by 5
Experimenting Text Summarization on Multimodal Aggregation.
Giuliano Armano, B., Giuliani, A., Messina, A., Montagnuolo, M., & Vargiu, E. (2011) · UNICA IRIS Institutional Research Information System (University of Cagliari) · Cited by 4
A Framework for Classifier Adaptation for Large-Scale Multimedia Data
Yang, J. J., Tong, W., & Hauptmann, A. G. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2204009 · Cited by 4
An Iterative Learning Framework for Multimodal Chlorophyll-a Estimation
Dávila, J., & Zaremba, M. B. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2599101 · Cited by 4
Analysis of multimodal fusion techniques for audio-visual speech recognition
Ivanko, D., Kipyatkova, I. S., Ronzhin, A. L., & Karpov, A. A. (2016) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2016-16-3-387-401 · Cited by 4
Tian, H., Chen, S., Rubin, S. H., & Grefe, W. K. (2017) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2017.20 · Cited by 4
MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation
Yang, C., Zhang, J., Gao, X., & Chen, G. (2021) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws53863.2021.00030 · Cited by 4
M2SN: Adaptive and Dynamic Multi-modal Shortcut Network Architecture for Latency-Aware Applications
Pu, Y., Wang, C., Hou, X., Xu, C., Liu, J., Wang, J., Guo, M., & Li, C. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687384 · Cited by 4
Efficient Multi-Modal Fusion with Diversity Analysis
Qu, S., Kang, Y., & Lee, J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475188 · Cited by 4
Multimodal Information Fusion for Semantic Video Analysis
Gulen, E., Yilmaz, T., & Yazıcı, A. (2012) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/jmdem.2012100103 · Cited by 4
A RELIEF-based modality weighting approach for multimodal information retrieval
Yilmaz, T., Gulen, E., Yazıcı, A., & Kitsuregawa, M. (2012) · International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324858 · Cited by 4
Towards Advanced Information Fusion for Driver Assistant Systems of Modern Vehicles
Dittmann, F., Geramani, K., Rigas, G., Katsis, C. D., & Fotiadis, D. I. (2008) · IEEE Vehicular Technology Conference · doi:10.1109/vetecf.2008.441 · Cited by 4
Application of Diffusion Kernel in Multimodal Image Retrieval
Agrawal, R. K., Grosky, W. I., Fotouhi, F. A., & Wu, C. (2007) · IEEE International Symposium on Multimedia Workshops (ISMW) · doi:10.1109/ism.workshops.2007.53 · Cited by 4
Requirements for benchmarking personal image retrieval systems
Bouguet, J.-Y., Dulong, C., Kozintsev, I. V., & Wu, Y. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.660254 · Cited by 4
Self-citationVideo content categorization using the double decomposition
Du, Y., Chen, F., Xu, W., & Qian, X. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1213-y · Cited by 3
Multi-Graph Semi-Supervised Learning for Video Semantic Feature Extraction
Wang, M., Hua, X., Yuan, X., Song, Y., & Dai, L.-R. (2007) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2007.4285066 · Cited by 3
Unified framework for representation, analysis of multimedia content for correlation and prediction
Paul, S., & Singh, Y. J. (2013) · National Conference on Emerging Trends and Applications in Computer Science (NCETACS) · doi:10.1109/icetacs.2013.6691425 · Cited by 3
A multimedia information fusion framework for web image categorization
Lu, W., Li, L., Li, J., Li, T., Zhang, H., & Guo, J. H. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1165-2 · Cited by 3
Compound Document Analysis by Fusing Evidence Across Media
Nikolopoulos, S., Lakka, C., Kompatsiaris, I. Y., Varytimidis, C., Rapantzikos, K. E., & Avrithis, Y. (2009) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2009.35 · Cited by 3
Exploring Interaction Between Images and Texts for Web Image Categorization
Li, L., Lu, W., Li, J., Li, T., Zhang, H., & Guo, J. H. (2011) · Cited by 3
Music Mood Classification Based on Lifelog
Tong, H., Zhang, M., Soleimaninejadian, P., Zhang, Q., Wu, K., Liu, Y., & Ma, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01012-6_5 · Cited by 3
An integrated statistical model for multimedia evidence combination
Gao, S., Lim, J., & Sun, Q. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291432 · Cited by 3
Temko, A., Macho, D., & Nadeu, C. (2006) · Lecture notes in computer science · doi:10.1007/11677482_31 · Cited by 3
A general framework for classifier adaptation and its applications in multimedia
Hauptmann, A. G., & Yang, J. (2009) · Cited by 2
Feature Relationships Hypergraph for Multimodal Recognition
Zhang, L., Song, M., Bian, W., Tao, D., Liu, X., Bu, J., & Chen, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24955-6_70 · Cited by 2
Hierarchical genetic fusion of possibilities
Souvannavong, F., & Huet, B. (2005) · doi:10.1049/ic.2005.0724 · Cited by 2
Cross-Modal Correlation Mining Using Graph Algorithms
Pan, J.-Y., Yang, H.-J., & Faloutsos, C. (2007) · Knowledge Discovery and Data Mining · doi:10.4018/978-1-59904-252-7.ch004 · Cited by 2
Wang, X., Rui, Y., & Kankanhalli, M. (2014) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2611777 · Cited by 2
Learning with low-quality data: multi-view semi-supervised learning with missing views
Huan, J., & Quanz, B. (2012) · Cited by 2
MMBypass: Towards efficient multi-modal AI computing with adaptive bypass network
Pu, Y., Xia, X., Hou, X., Wang, C., Xu, C., Liu, J., Wang, J., Guo, M., Yuan, J., & Li, C. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105078 · Cited by 1
Pu, Y., Xia, X., Hou, X., Xu, C., Liu, J., Li, C., & Guo, M. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108814 · Cited by 1
Wang, X., Rui, Y., & Kankanhalli, M. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2071945 · Cited by 1
The future of consumer cameras
Battiato, S., & Moltisanti, M. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083853 · Cited by 1
A New Index Method for Large Motion Capture Data
Zhu, H., & Xiang, J. (2007) · International Symposium on Data, Privacy, and E-Commerce · doi:10.1109/isdpe.2007.108 · Cited by 1
Multimodal kernel learning for image retrieval
Lin, Y., & Fuh, C. (2010) · International Conference on System Science and Engineering · doi:10.1109/icsse.2010.5551790 · Cited by 1
Typicality ranking: beyond accuracy for video semantic annotation
Tang, J., & Hua, X. (2011) · Multimedia Tools and Applications · doi:10.1007/s11042-011-0892-0 · Cited by 1
Content-based visual search learned from social media
Li, X. (2012) · ACM SIGMultimedia Records · doi:10.1145/2206765.2206774 · Cited by 1
Interactive Object Annotation for Construction of Video Information System
Wu, X., Kamijo, S., Zhang, W., & Sakauchi, M. (2006) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2006.97 · Cited by 1
Converting Raw Sensor Data to Semantic Web Triples: A Survey of Implementation Options
Κωνσταντίνου, Ν. (2012) · International Journal of Sensors Wireless Communications and Control · doi:10.2174/2210327911202010044 · Cited by 1
A Selective Weighted Late Fusion for Visual Concept Recognition
Liu, N., Dellandréa, E., Tellez, B., & Chen, L. (2014) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-05696-8_1 · Cited by 1
Interactive Representations of Multimodal Databases
Marchand‐Maillet, S., Morrison, D., Székely, E., & Bruno, É. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-374825-6.00006-x · Cited by 1
State Machine Based Method for Consolidating Vehicle Data
Dittmann, F., Geramani, K., Fäßler, V., & Damiani, S. (2009) · IFIP advances in information and communication technology · doi:10.1007/978-3-642-04284-3_1 · Cited by 1
Continuous Behaviour Knowledge Space For Semantic Indexing of Video Content
Souvannavong, F., & Huet, B. (2006) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2006.301568 · Cited by 1
Automated Modelling of Multimodal Data Processes in Remote Sensing
Dávila, J., & Zaremba, M. B. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.367 · Cited by 1
Heterogeneous massive feature fusion on grassmannian manifold
Huang, H., Liu, H., Kong, X., Lou, X., & Wang, Z. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/887/1/012066 · Cited by 1
PAC-Bayesian Majority Vote for Late Classifier FusionPreprint
Morvant, E., Habrard, A., & Ayache, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.1019 · Cited by 1
Integration of content and context modalities for multimedia big data retrieval
Zhu, Q. (2014) · Cited by 1
Interoperable and unified multimedia retrieval in distributed and heterogeneous environments
Stegmaier, F. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874330 · Cited by 1
Application of Diffusion Kernel in Multimodal Image Retrieval
Agrawal, R. K., Grosky, W. I., Fotouhi, F. A., & Wu, C. (2007) · doi:10.1109/ismw.2007.14 · Cited by 1
Ren, R., Halvey, M. J., & Jose, J. M. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202628 · Cited by 1
Exploiting inter-conceptual relationships to boost SVM classification .
Poulisse, G.-J. (2007) · Cited by 1
Query Generation from Multiple Media Examples
Ren, R., & Jose, J. M. (2009) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2009.13 · Cited by 1
Content‐Based Retrieval of Videos
Chen, M., Chen, S., & Shyu, M. (2010) · doi:10.1002/9780470588222.ch3 · Cited by 1
Web-Scale Multimedia Data Management: Challenges and Remedies
Chang, E. Y. (2007) · doi:10.1109/iciapw.2007.47 · Cited by 1
Self-citationDEEP LEARNING FOR DATA-DRIVEN DECISION-MAKING
Wan, Z. (2020) · Digital Commons - URI (University of Rhode Island) · doi:10.23860/wan-zhiqiang-2020 · Cited by 1
Chang, E. Y. (2011) · Springer eBooks · doi:10.1007/978-3-642-20429-6_6 · Cited by 1
Self-citationGenerating Linked Data in Real-time from Sensor Data Streams
Κωνσταντίνου, Ν., & Spanos, D.-E. (2015) · Springer eBooks · doi:10.1007/978-3-319-16074-0_5
Nayar, H., & Nadakuditi, R. R. (2015)
Metadata
- Authors
- Wu, Y., Chang, E. Y., Chang, K. C.-C., & Smith, J. R.
- Affiliations
- University of California, Santa Barbara (US)
- University of Illinois Urbana-Champaign (US)
- IBM (United States) (US)
- Venue
- Proceedings of the 12th annual ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2004-10-10
- Pages
- 572-579
- OpenAlex
- W2056952330
- Cited by
- 229
- References
- 35
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 5 · 2019: 8 · 2018: 5 · 2017: 6 · 2016: 7 · 2015: 9 · 2014: 12 · 2013: 15 · 2012: 24
- FWCI
- 14.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Science Foundation
- National Institute of Standards and Technology
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- multimodal fusion, feature fusion, modality selection, curse of dimensionality, video captioning, multimedia data analysis, class prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
