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Separating Style and Content with Bilinear Models
Abstract
Perceptual systems routinely separate “content” from “style,” classifying familiar words spoken in an unfamiliar accent, identifying a font or handwriting style across letters, or recognizing a familiar face or object seen under unfamiliar viewing conditions. Yet a general and tractable computational model of this ability to untangle the underlying factors of perceptual observations remains elusive (Hofstadter, 1985). Existing factor models (Mardia, Kent, & Bibby, 1979; Hinton & Zemel, 1994; Ghahramani, 1995; Bell & Sejnowski, 1995; Hinton, Dayan, Frey, & Neal, 1995; Dayan, Hinton, Neal, & Zemel, 1995; Hinton & Ghahramani, 1997) are either insufficiently rich to capture the complex interactions of perceptually meaningful factors such as phoneme and speaker accent or letter and font, or do not allow efficient learning algorithms. We present a general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nonetheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization. We report promising results on three different tasks in three different perceptual domains: spoken vowel classification with a benchmark multi-speaker database, extrapolation of fonts to unseen letters, and translation of faces to novel illuminants.
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“GeAN also uses the bilinear model [29,30] to distil the knowledge interwoven in these representations by allowing each node and edge to collaboratively modulate each other's contributions through a shared learning space.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [29]
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040
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
Shape and motion from image streams under orthography: a factorization method
Tomasi, C., & Takeo Kanade, A. (1992) · International Journal of Computer Vision · doi:10.1007/bf00129684 · Cited by 2,879
Matrix Differential Calculus with Applications in Statistics and Econometrics.
Magnus, J. R., & Neudecker, H. (1988) · Biometrics · doi:10.2307/2531754 · Cited by 2,729
Classical Electrodynamics (2nd edn)
Brereton, M. (1976) · Physics Bulletin · doi:10.1088/0031-9112/27/8/041 · Cited by 2,612
Cited by
Sorted by citations
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,174
“, AN be m × 3 matrices whose columns are images of object i (from the bootstrap set) that contain the same m pixels; then the bilinear energy/cost function [Freeman and Tenenbaum 2000] for an image ys of object y under illumination s is ( ys − N ∑”
BackgroundImage Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025
“Such factorised representations were previously achieved only for controlled subsets of natural images such as faces under different illumination conditions and characters in different font styles [29] or handwritten digits and house numbers [17].”
BackgroundA Neural Algorithm of Artistic Style
Gatys, L., Ecker, A., & Bethge, M. (2016) · Journal of Vision · doi:10.1167/16.12.326 · Cited by 2,612
“12,13 In our demonstration, we render a given photograph in the style of a range of well-known artworks.”
BackgroundBilinear CNN Models for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234
“Bilinear models were proposed by Tanenbaum and Freeman [33] to model two-factor variations, such as “style” and “content”, for images.”
MethodMultimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582
Gao, Y., Beijbom, O., Zhang, N., & Darrell, T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.41 · Cited by 919
“Bilinear models were first introduced by Tenenbaum and Freeman [32] to separate style and content.”
MethodMultilinear Analysis of Image Ensembles: TensorFaces
Vasilescu, M. A. O., & Terzopoulos, D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47969-4_30 · Cited by 850
“This is not to be confused with the classical “rank-R problem” [7].”
BackgroundGeneralized Multiview Analysis: A discriminative latent space
Sharma, A., Kumar, A., Daumé, H., & Jacobs, D. W. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247923 · Cited by 737
“• BLM A i = X i W i X T i , B i = I and Z i = X i i.e. same as GMPCA.”
MethodInfluentialWeighted low-rank approximations
Srebro, N., & Jaakkola, T. (2003) · International Conference on Machine Learning · Cited by 715
Learning photographic global tonal adjustment with a database of input/output image pairs
Bychkovsky, V., Paris, S., Chan, E., & Durand, F. (2011) · doi:10.1109/cvpr.2011.5995413 · Cited by 666
A Framework for Robust Subspace Learning
de la Torre, F. N., & Black, M. J. (2003) · International Journal of Computer Vision · doi:10.1023/a:1023709501986 · Cited by 601
“One example is the work of Tenenbaum and Freeman on factoring style and content (Tenenbaum and Freeman, 2000) using a bilinear model.”
MethodGenerative Image Modeling Using Style and Structure Adversarial Networks
Wang, X., & Gupta, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_20 · Cited by 615
“Finally, the idea of factorizing image into two separate phenomena has been well studied in [46,47,48,49], which motivates us to decompose the generative process to structure and style.”
BackgroundParametric hidden Markov models for gesture recognition
Wilson, A. D., & Bobick, A. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.790429 · Cited by 574
Deep Learning of Representations: Looking Forward
Bengio, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39593-2_1 · Cited by 568
“…priors on the form of the model can be used to enhance disentangling, such as multiplicative interactions between the factors (Tenenbaum and Freeman, 2000; Desjardins et al., 2012) or orthogonality of the features derivative with respect to the input (Rifai et al., 2011b, 2012a; Sohn et…”
MethodLearning photographic global tonal adjustment with a database of input / output image pairs
Bychkovsky, V., Paris, S., Chan, E., & Durand, F. (2011) · doi:10.1109/cvpr.2011.5995332 · Cited by 531
Learning a Mahalanobis Metric from Equivalence Constraints
Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2005) · Cited by 517
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
Brand, M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47969-4_47 · Cited by 511
“Examples include the combination of lighting and pose [20] and shape and motion [12,3] in image formation, mixing of sources in acoustic recordings [6], and word associations in collections of documents [1,23].”
BackgroundMultimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511
“This method is also referred to as second-order pooling [167].”
MethodProbabilistic Linear Discriminant Analysis
Ioffe, S. (2006) · Lecture notes in computer science · doi:10.1007/11744085_41 · Cited by 499
“Note that if an example comes from a class represented by v , and belongs to the j th mixture component, then its expected value is m j + A j v , which is the representation used in asymmetric bi-linear models [12].”
MethodLearning Discriminative Binary Codes for Large-scale Cross-modal Retrieval
Xu, X., Shen, F., Yang, Y., Shen, H. T., & Li, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2676345 · Cited by 458
“Despite of the above classical methods such as CCA and BLM, other subspace learning approaches leverage the valuable class label information to improve the retrieval performance.”
BackgroundAudio-driven facial animation by joint end-to-end learning of pose and emotion
Karras, T., Aila, T., Laine, S., Herva, A., & Lehtinen, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073658 · Cited by 435
Learning distance functions using equivalence relations
Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2003) · Cited by 433
How Close Are We to Understanding V1?
Olshausen, B. A., & Field, D. J. (2005) · Neural Computation · doi:10.1162/0899766054026639 · Cited by 421
Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch
Sharma, A., & Jacobs, D. W. (2011) · doi:10.1109/cvpr.2011.5995350 · Cited by 384
Multilinear subspace analysis of image ensembles
Vasilescu, M. A. O., & Terzopoulos, D. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211457 · Cited by 379
“It subsumes as special cases the simple linear (1-factor) analysis known as principal components analysis (PCA), as well as bilinear (2-factor) analysis [7].”
MethodLow-Rank Bilinear Pooling for Fine-Grained Classification
Kong, S., & Fowlkes, C. C. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.743 · Cited by 374
Wang, T., Zhao, J., Yatskar, M., Chang, K.-W., & Ordóñez, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00541 · Cited by 353
“Building models under fairness objectives is also related to feature disentangling methods [28, 22, 17, 18, 19].”
BackgroundKernel Pooling for Convolutional Neural Networks
Cui, Y., Zhou, F., Wang, J., Liu, X., Lin, Y., & Belongie, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.325 · Cited by 360
“In the case of p = 2, the model is usually referred as Bilinear models, first introduced by Tenenbaum and Freeman [40].”
MethodMultilinear Discriminant Analysis for Face Recognition
Yan, S., Xu, D., Yang, Q., et al. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.884929 · Cited by 340
“Most traditional algorithms, such as the principal component analysis (PCA) [17], [20], [21], [24] and linear discriminant analysis (LDA)”
MethodDisentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
Deng, Y., Yang, J., Chen, D., Wen, F., & Tong, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00520 · Cited by 329
“Historical attempts are based on simple bilinear models [46], restricted Boltzmann machines [10, 39], among others.”
Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723400 · Cited by 408
“Bilinear models were used by Tanenbaum and Freeman [48] to model twofactor variations such as “style” and “content” for images.”
MethodLearning to share visual appearance for multiclass object detection
Salakhutdinov, R., Torralba, A. B., & Tenenbaum, J. B. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995720 · Cited by 330
“These methods can be applied for object classes with well defined sets of transformations such as letters [20, 28], or faces [28].”
MethodSelf-citationJoint Feature Selection and Subspace Learning for Cross-Modal Retrieval
Wang, K., He, R., Wang, L., Wang, W., & Tan, T. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2505311 · Cited by 318
“Recently, several approaches for establishing inter-modal relationships between data from different modalities generally rely on subspace learning, such as Canonical Correlation Analysis [25], [26], Partial Least Squares (PLS) [27] and Bilinear Model (BLM) [28], [29].”
BackgroundMethodMulti-view Harmonized Bilinear Network for 3D Object Recognition
Yu, T., Meng, J., & Yuan, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00027 · Cited by 318
“Bilinear pooling is first proposed by Tenenbaum and Freeman [31] to separate style and content.”
Style translation for human motion
Hsu, E., Pulli, K., & Popović, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073315 · Cited by 309
RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods
Herath, S., Yan, H. R., & Furukawa, Y. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196860 · Cited by 376
“RoNIN LSTM : We use a stacked unidirectional LSTM [18] while enriching its input feature by concatenating the output of a bilinear layer [20].”
MethodNorouzi, M., & Fleet, D. J. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.388 · Cited by 306
“, [17]).”
Applications of Support Vector Machines to Speech Recognition
Ganapathiraju, A., Hamaker, J., & Picone, J. (2004) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.831018 · Cited by 281
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding
Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., & Du, B. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2014.12.016 · Cited by 279
“In this subsection, we use theWiki Text-Image data set to illustrate the effectiveness of EMR-SLRA algorithm in comparing with its competitors including the traditional multiview methods CCA, PLS and BLM. Table 1 shows the number of training and test samples of each category in the Wiki Text-Image data set.”
MethodInfluentialArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
An, J., Huang, S., Song, Y., Dou, D., Liu, W., & Luo, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00092 · Cited by 276
“Following the theoretical framework of the Bilinear Model [52], we can define the unbiased style transfer as: Definition 1 Suppose we have a bilinear style transfer module f cs = C ( f c ) S ( f s ) , where C, S denote the content factor and the style factor in the bilinear model, respectively. f cs is an unbiased style…”
BackgroundMethodInfluentialDeep Cascaded Bi-Network for Face Hallucination
Zhu, S., Liu, S., Loy, C. C., & Tang, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_37 · Cited by 249
Weakly-supervised Disentangling with Recurrent Transformations for 3D View SynthesisPreprint
Yang, J., Reed, S., Yang, M., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.00706 · Cited by 247
“Bilinear models for separating style and content are developed in [25], and are shown to”
MethodDiscovering states and transformations in image collections
Isola, P., Lim, J. J., & Adelson, E. H. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298744 · Cited by 243
“This approach is reminiscent of Freeman and Tenenbaum’s work on separating style and content [29].”
ResultLearning Coupled Feature Spaces for Cross-Modal Matching
Wang, K., He, R., Wang, W., et al. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.261 · Cited by 238
“In [24], Tenenbaum and Freeman propose a bilinear model (BLM) to derive a common space for cross-modal face recognition, and BLM is also used for text-image retrieval in [22].”
BackgroundMethodInfluentialDeep Temporal Linear Encoding Networks
Diba, A., Sharma, V., & Gool, L. V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.168 · Cited by 238
“Likewise, Bilinear models [20, 32] have been widely used and have achieved state-of-the-art results.”
MethodEntity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification
Yu, J., Jiang, J., & Xia, R. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2957872 · Cited by 235
“the widely used bilinear models [35], which is expected to consider all the pairwise interactions between features.”
MethodMultimodal Neurons in Artificial Neural Networks
Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., Radford, A., & Olah, C. (2021) · Distill · doi:10.23915/distill.00030 · Cited by 330
Personalized recommendation on dynamic content using predictive bilinear models
Chu, W., & Park, S.-T. (2009) · The Web Conference · doi:10.1145/1526709.1526802 · Cited by 288
“[39] J. B. Tenenbaum and W. T. Freeman.”
BackgroundMultilinear image analysis for facial recognition
Vasilescu, M. A. O., & Terzopoulos, D. (2003) · doi:10.1109/icpr.2002.1048350 · Cited by 231
Disentangling factors of variation in deep representations using adversarial trainingPreprint
Mathieu, M., Zhao, J., Sprechmann, P., Ramesh, A., & LeCun, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.03383 · Cited by 263
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
Memisevic, R., & Hinton, G. E. (2010) · Neural Computation · doi:10.1162/neco.2010.01-09-953 · Cited by 231
Swapping Autoencoder for Deep Image ManipulationPreprint
Park, T., Zhu, J.-Y., Wang, O., Lu, J., Shechtman, E., Efros, A. A., & Zhang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00653 · Cited by 263
“Modeling style and content is a classic computer vision and graphics problem [69, 22].”
BackgroundLearning Consistent Feature Representation for Cross-Modal Multimedia Retrieval
Kang, C., Xiang, S., Liao, S., Xu, C., & Pan, C. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2390499 · Cited by 222
“There are some other algorithms which can also deal with cross-modal problems, such as BLM [38].”
BackgroundMethodMTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
Liu, X., Hu, Z., Ling, H., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2940446 · Cited by 221
“Accordingly, many reasonable extensions, e.g., bi-linear model (BLM) [9], manifold learning [9], latent subspace analysis (LSA) [1], sparse subspace learning (SSL) [5], [10], and correlated subspace learning (CSL) [2], [11], have also been developed for cross-modal retrieval task.”
BackgroundLearning with matrix factorizations
Srebro, N., & Jaakkola, T. (2004) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 217
Manifold based analysis of facial expression
Chang, Y. H., Hu, C., Feris, R. S., & Turk, M. (2005) · Image and Vision Computing · doi:10.1016/j.imavis.2005.08.006 · Cited by 214
“Tenenbaum and Freeman [33] talked about separating style and content by using symmetry or asymmetry bilinear models.”
BackgroundSynthesizing retinal and neuronal images with generative adversarial nets
Zhao, H., Li, H., Maurer‐Stroh, S., & Cheng, L. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.07.001 · Cited by 216
Unifying Nuclear Norm and Bilinear Factorization Approaches for Low-Rank Matrix Decomposition
Cabral, R., de la Torre, F. N., Costeira, J. P., et al. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.309 · Cited by 210
“However, this is not generalizable to several other computer vision problems modeled as lowrank factorization problems [5, 31, 34, 37].”
BackgroundTransformation-invariant clustering using the EM algorithm
Frey, B. J., & Jojić, N. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1159942 · Cited by 207
Statistical regularities in art: Relations with visual coding and perception
Graham, D. J., & Redies, C. (2010) · Vision Research · doi:10.1016/j.visres.2010.05.002 · Cited by 216
“This challenge is part of the more general problem in computer vision of separating style and content (see Tenenbaum & Freeman, 2000).”
BackgroundLearning to Disentangle Factors of Variation with Manifold Interaction
Reed, S., Sohn, K., Zhang, Y., & Lee, H. (2014) · Cited by 201
Facial expression decomposition
Wang, H., & Ahuja (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238452 · Cited by 199
“[1] propose using a bilinear model [2] to separate person-specific video data into expressive features and speech content, and synthesize new sequences which maintain the original speech while using a different expression.”
MethodReed, S., Zhang, Y., Zhang, Y., & Lee, H. (2015) · neural information processing systems · Cited by 197
Tied Factor Analysis for Face Recognition across Large Pose Differences
Prince, S. J. D., Warrell, J., Elder, J. H., et al. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.48 · Cited by 193
“Tenenbaum and Freeman [32] investigated the use of bilinear models for separating style from content.”
BackgroundInfluentialDeep Learning Techniques for Future Intelligent Cross-Media RetrievalPreprint
Rehman, S. U., Waqas, M. M., Tu, S., Koubâa, A., Rehman, O. U., Ahmad, J. E., Hanif, M. I., & Han, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01191 · Cited by 191
“Generalized Multiview Analysis (GMA) [13] can be used to combine CCA, BLM, and PLS for solving cross-media retrieval task.”
BackgroundInfluentialBilinear Models for 3-D Face and Facial Expression Recognition
Mpiperis, I., Malassiotis, S., & Strintzis, M. G. (2008) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2008.924598 · Cited by 185
Separating style and content on a nonlinear manifold
Elgammal, A., & Lee, C.-S. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315070 · Cited by 182
Tensor Methods in Computer Vision and Deep Learning
Panagakis, Y., Kossaifi, J., Chrysos, G. G., Oldfield, J. P., Nicolaou, M. A., Anandkumar, A., & Zafeiriou, S. P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3074329 · Cited by 209
Matsubara, T., & Morimoto, J. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2250502 · Cited by 180
“, multiuser interface, using the style and content separation approach [21].”
BackgroundMethodInfluentialMiME: Multilevel Medical Embedding of Electronic Health Records for Predictive HealthcarePreprint
Choi, E., Xiao, C., Stewart, W. F., & Sun, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09593 · Cited by 192
“(3) was inspired by recent developments in bilinear pooling technique [37, 21, 19, 24], which we discuss in more detail in Appendix A.”
BackgroundUnsupervised Learning of Image Transformations
Memisevic, R., & Hinton, G. E. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383036 · Cited by 177
“There is some resemblance between our approach and the bilinear model of style and content proposed in [18], but the learning methods are quite different and our approach allows the transformations to be highly non-linear functions of the data.”
ResultHuang, D.-A., & Wang, Y.-C. F. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.310 · Cited by 174
“Besides considering CCA as the baseline approach, we consider the methods of Tang & Wang [17], PLS [16], bilinear model [18], SCDL [19], and joint dictionary learning Figure 7.”
BackgroundMethodCombining multiple sources of knowledge in deep CNNs for action recognition
Park, E., Han, X., Berg, T. L., & Berg, A. C. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477589 · Cited by 169
Expressive speech-driven facial animation
Cao, Y., Tien, W. C., Faloutsos, P., & Pighin, F. P. (2005) · ACM Transactions on Graphics · doi:10.1145/1095878.1095881 · Cited by 166
Dumoulin, V., Perez, E., Schucher, N., Strub, F., de Vries, H., Courville, A. C., & Bengio, Y. (2018) · Distill · doi:10.23915/distill.00011 · Cited by 192
Towards Robust Pattern Recognition: A Review
Zhang, X.-Y., Liu, C., & Suen, C. Y. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2989782 · Cited by 172
“Content and style [276] are two important factors in pattern recognition.”
BackgroundAdaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval
Yu, E., Sun, J., Li, J., et al. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2877127 · Cited by 157
“Unsupervised subspace learning methods merely leverage the pairwise information of unlabeled samples from different modalities to learn the common latent subspace [14], [18], [20], [21].”
BackgroundAhrens, M. B., Linden, J. F., & Sahani, M. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.3377-07.2008 · Cited by 151
“Other multilinear methods have been useful in various settings in the statistics and machine learning setting (e.g. Vasilescu and Terzopoulos, 2005; Tenenbaum and Freeman, 2000; Harshman and Lundy, 1994).”
Cluster Canonical Correlation Analysis
Rasiwasia, N., Mahajan, D. K., Mahadevan, V., & Aggarwal, G. (2014) · Cited by 152
Bilinear classifiers for visual recognition
Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2009) · doi:10.13016/m2uvdv-qzxg · Cited by 141
Discovering Hidden Factors of Variation in Deep NetworksPreprint
Cheung, B., Livezey, J. A., Bansal, A. K., & Olshausen, B. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6583 · Cited by 152
“Previous approaches to separating factors of variation in data, such as content vs. style [3] or form vs. motion [4, 5, 6, 7, 8, 9], have relied upon a bilinear model architecture in which the units representing different factors are combined multiplicatively.”
MethodGeneralized Semi-supervised and Structured Subspace Learning for Cross-Modal Retrieval
Zhang, L., Ma, B., Li, G., et al. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2723841 · Cited by 139
“The unsupervised cross-modal methods use paired training samples between two modalities to learn the common subspace, including Partial Least Squares (PLS) [21], Canonical Correlation Analysis (CCA) [19], Bilinear Model (BLM) [22], Kernel CCA (KCCA) [19], and Deep CCA (DCCA) [2].”
MethodWang, Z., Rui-qing, L., Wang, M., & Li, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab185 · Cited by 140
Cross-Modal Retrieval via Deep and Bidirectional Representation Learning
He, Y., Xiang, S., Kang, C., Wang, J., & Pan, C. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2558463 · Cited by 137
“There exist some classic algorithms for constructing a common space, such as canonical correlation analysis (CCA) [19], partial least squares (PLS) [20], bilinear models (BLM) [21] and generalized multiview analysis (GMA) [22].”
MethodHuman motion signatures: analysis, synthesis, recognition
Vasilescu, M. A. O. (2003) · doi:10.1109/icpr.2002.1047975 · Cited by 134
Content-based image retrieval with compact deep convolutional features
Alzu’bi, A., Amira, A., & Ramzan, N. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.072 · Cited by 138
“The proposed architecture is inspired by the bilinear models proposed by Tenenbaum and Freeman [15] to model the separation between the “content” and “style” factors of perceptual systems and by the promising results obtained using bilinear CNNs applied to fine-grained categorization [16].”
High Resolution Acquisition, Learning and Transfer of Dynamic 3‐D Facial Expressions
Wang, Y., Huang, X., Lee, C.-S., et al. (2004) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2004.00800.x · Cited by 133
Natural facial expression recognition using differential-AAM and manifold learning
Cheon, Y., & Kim, D. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.10.010 · Cited by 132
“Linear models such as principal component analysis (PCA) [2], bilinear [3], and tensor model [4] are simple and efficient [5].”
MethodMultilinear Multitask Learning
Romera‐Paredes, B., Aung, M. H., Bianchi‐Berthouze, N., & Pontil, M. (2013) · UCL Discovery (University College London) · Cited by 130
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
Singh, K. K., Ojha, U., & Lee, Y. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00665 · Cited by 132
“Disentangled representation learning has a vast literature (e.g., [3, 46, 22, 51, 9, 21, 11, 23]).”
BackgroundMultifactor Gaussian process models for style-content separation
Wang, J. M.-C., Fleet, D. J., & Hertzmann, A. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273619 · Cited by 126
Wu, L. Y., Wang, Y., Li, X., & Gao, J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.004 · Cited by 127
“Bilinear models [50] can model two-factor variations and provide richer representations than linear models.”
BackgroundBilinear spatiotemporal basis models
Akhter, I., Simon, T., Khan, S., Matthews, I. A., & Sheikh, Y. A. (2012) · ACM Transactions on Graphics · doi:10.1145/2159516.2159523 · Cited by 121
Describing Visual Scenes using Transformed Dirichlet Processes
Torralba, A. B., Willsky, A. S., Sudderth, E. B., & Freeman, W. T. (2005) · neural information processing systems · Cited by 122
Self-citationSemantic Label Sharing for Learning with Many Categories
Fergus, R., Bernal, H. J., Weiss, Y., & Torralba, A. B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15549-9_55 · Cited by 117
“Other approaches transfer information across object categories by sharing a common set of parts [16, 17], by sharing transformations across different instances [18–20], or by sharing a set of prototypes [21].”
BackgroundTwo Distributed-State Models For Generating High-Dimensional Time Series
Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2011) · doi:10.5555/1953048.2021035 · Cited by 114
“Therefore at first glance, the model appears similar to the bilinear factor model (Tenenbaum and Freeman, 2000).”
MethodHuman Carrying Status in Visual Surveillance
Tao, D., Li, X., Maybank, S. J., et al. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.138 · Cited by 114
Few-shot Font Generation with Localized Style Representations and Factorization
Park, S., Chun, S., Cha, J., Lee, B., & Shim, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16340 · Cited by 117
Are Disentangled Representations Helpful for Abstract Visual Reasoning?Preprint
van Steenkiste, S., Locatello, F., Schmidhuber, J., & Bachem, O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12506 · Cited by 127
“Disentangling pose, style, or motion from content are classical vision tasks that has been studied with different degrees of supervision [72, 79, 80, 34, 19, 14, 21, 36].”
BackgroundMood swings: expressive speech animation
Chuang, E. S., & Bregler, C. (2005) · ACM Transactions on Graphics · doi:10.1145/1061347.1061355 · Cited by 114
Metadata
- Authors
- Tenenbaum, J. B., & Freeman, W. T.
- Affiliations
- Massachusetts Institute of Technology (US)
- Mitsubishi Electric Research Laboratories (United States) (US)
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2000-06-01
- Volume
- 12
- Issue
- 6
- Pages
- 1247-1283
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2170653751
- Semantic Scholar
- 7e85f7d59e37972ec52cbabfef0512588d87f125
- Cited by
- 840
- References
- 61
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 20 · 2023: 31 · 2022: 38 · 2021: 61 · 2020: 58 · 2019: 70 · 2018: 49 · 2017: 39 · 2016: 56 · 2015: 34 · 2014: 27 · 2013: 39 · 2012: 30
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- bilinear model, factor models, perceptual learning, speaker accent, singular value decomposition, expectation-maximization algorithm, factor interactions
- MeSH terms
- Algorithms, Face, Humans, Learning, Pattern Recognition, Visual, Speech Perception, Linear Models, Neural Networks, Computer
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