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Domain Adaptation for Visual Understanding

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Domain Adaptation for Visual Understanding

Abstract

Advances in visual understanding in the last two decades have been aided by exemplary progress in machine learning and deep learning methods. One of the principal issues of modern classifiers is generalization toward unseen testing data which may have a distribution different to that of the training set. Further, classifiers need to be adapted to scenarios where training data is made available online. Domain adaptation based machine learning algorithms cater to these specific scenarios where the classifiers are updated for inclusivity and generalizability. Such methods need to encompass the covariate shift so that the trained model gives appreciable performance on the testing data. In this chapter, we categorize, illustrate, and analyze different domain adaptation based machine learning algorithms for visual understanding.

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References

  1. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

  2. Singh R, Vatsa M, Ross A, Noore A (2010) Biometric classifier update using online learning: a case study in near infrared face verification. Image Vis Comput 28(7):1098–1105

    Article  Google Scholar 

  3. Bharadwaj S, Bhatt HS, Singh R, Vatsa M, Noore A (2015) Qfuse: online learning framework for adaptive biometric system. Pattern Recognit 48(11):3428–3439

    Article  Google Scholar 

  4. Singh R, Vatsa M, Ross A, Noore A (2009) Online learning in biometrics: a case study in face classifier update. In: International conference on biometrics: theory, applications, and systems, pp 1–6

    Google Scholar 

  5. Mehrotra H, Singh R, Vatsa M, Majhi B (2016) Incremental granular relevance vector machine: a case study in multimodal biometrics. Pattern Recognit 56:63–76

    Article  Google Scholar 

  6. Chen JC, Patel VM, Chellappa R (2016) Unconstrained face verification using deep CNN features. In: IEEE winter conference on applications of computer vision, pp 1–9

    Google Scholar 

  7. Chen YC, Patel VM, Phillips PJ, Chellappa R (2012) Dictionary-based face recognition from video. In: European conference on computer vision, pp 766–779

    Chapter  Google Scholar 

  8. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  9. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13

    Article  Google Scholar 

  10. Li LJ, Socher R, Fei-Fei L (2009) Towards total scene understanding: classification, annotation and segmentation in an automatic framework. In: IEEE conference on computer vision and pattern recognition, pp 2036–2043

    Google Scholar 

  11. Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artif Intell Rev 43(1):1–54

    Article  Google Scholar 

  12. da Fontoura Costa L, Cesar RM Jr (2010) Shape analysis and classification: theory and practice. CRC Press, Boca Raton

    Book  Google Scholar 

  13. Rui Y, Huang TS, Chang SF (1999) Image retrieval: current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39–62

    Article  Google Scholar 

  14. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: International conference on pervasive computing, pp 1–17

    Google Scholar 

  15. Bhatt HS, Singh R, Vatsa M, Ratha NK (2014) Improving cross-resolution face matching using ensemble-based co-transfer learning. IEEE Trans Image Process 23(12):5654–5669

    Article  MathSciNet  Google Scholar 

  16. Singh M, Nagpal S, Vatsa M, Singh R, Majumdar A (2018) Identity aware synthesis for cross resolution face recognition. In: IEEE conference on computer vision and pattern recognition workshops, pp 479–488

    Google Scholar 

  17. Dhamecha TI, Sharma P, Singh R, Vatsa M (2014) On effectiveness of histogram of oriented gradient features for visible to near infrared face matching. In: International conference on pattern recognition, pp 1788–1793

    Google Scholar 

  18. Ghosh S, Dhamecha TI, Keshari R, Singh R, Vatsa M (2015) Feature and keypoint selection for visible to near-infrared face matching. In: International conference on biometrics theory, applications and systems, pp 1–7

    Google Scholar 

  19. Mudunuri SP, Biswas S (2016) Low resolution face recognition across variations in pose and illumination. IEEE Trans Pattern Anal Mach Intell 38(5):1034–1040

    Article  Google Scholar 

  20. Yadav D, Singh R, Vatsa M, Noore A (2014) Recognizing age-separated face images: humans and machines. PloS One 9(12):1122–1134

    Google Scholar 

  21. Dhamecha TI, Singh R, Vatsa M, Kumar A (2014) Recognizing disguised faces: human and machine evaluation. PloS One 9(7):e99212

    Article  Google Scholar 

  22. Kushwaha V, Singh M, Singh R, Vatsa M, Ratha N, Chellappa R (2018) Disguised faces in the wild. In: IEEE conference on computer vision and pattern recognition workshops, pp 1–9

    Google Scholar 

  23. Nguyen HV, Ho HT, Patel VM, Chellappa R (2015) Dash-n: joint hierarchical domain adaptation and feature learning. IEEE Trans Image Process 24(12):5479–5491

    Article  MathSciNet  Google Scholar 

  24. Shrivastava A, Shekhar S, Patel VM (2014) Unsupervised domain adaptation using parallel transport on grassmann manifold. In: IEEE winter conference on applications of computer vision, pp 277–284

    Google Scholar 

  25. Shekhar S, Patel VM, Nguyen HV, Chellappa R (2013) Generalized domain-adaptive dictionaries. In: 2013 IEEE conference on computer vision and pattern recognition, pp 361–368

    Google Scholar 

  26. Qiu Q, Patel VM, Turaga P, Chellappa R (2012) Domain adaptive dictionary learning. In: European conference on computer vision, pp 631–645

    Chapter  Google Scholar 

  27. Zhang H, Patel VM, Shekhar S, Chellappa R (2015) Domain adaptive sparse representation-based classification. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), vol 1, pp 1–8

    Google Scholar 

  28. Bharadwaj S, Bhatt HS, Vatsa M, Singh R (2016) Domain specific learning for newborn face recognition. IEEE Trans Inf Forensics Secur 11(7):1630–1641

    Article  Google Scholar 

  29. Bharadwaj S, Bhatt HS, Singh R, Vatsa M, Singh SK (2010) Face recognition for newborns: a preliminary study. In: IEEE international conference on biometrics: theory, applications and systems, pp 1–6

    Google Scholar 

  30. Yin X, Han J, Yang J, Philip SY (2006) Efficient classification across multiple database relations: a crossmine approach. IEEE Trans Knowl Data Eng 18(6):770–783

    Article  Google Scholar 

  31. Kuncheva LI, Rodriguez JJ (2007) Classifier ensembles with a random linear oracle. IEEE Trans Knowl Data Eng 19(4):500–508

    Article  Google Scholar 

  32. Baralis E, Chiusano S, Garza P (2008) A lazy approach to associative classification. IEEE Trans Knowl Data Eng 20(2):156–171

    Article  Google Scholar 

  33. Shimodaira H (2000) Improving predictive inference under covariate shift by weighting the log-likelihood function. J Stat Plan Inference 90(2):227–244

    Article  MathSciNet  Google Scholar 

  34. Pan SJ, Yang Q et al (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  35. Socher R, Ganjoo M, Manning CD, Ng A (2013) Zero-shot learning through cross-modal transfer. In: Advances in neural information processing systems, pp 935–943

    Google Scholar 

  36. Palatucci M, Pomerleau D, Hinton GE, Mitchell TM (2009) Zero-shot learning with semantic output codes. In: Advances in neural information processing systems, pp 1410–1418

    Google Scholar 

  37. Romera-Paredes B, Torr P (2015) An embarrassingly simple approach to zero-shot learning. In: International conference on machine learning, pp 2152–2161

    Google Scholar 

  38. Raina R, Battle A, Lee H, Packer B, Ng AY (2007) Self-taught learning: transfer learning from unlabeled data. In: International conference on machine learning, pp 759–766

    Google Scholar 

  39. Blanz V, Schölkopf B, Bülthoff H, Burges C, Vapnik V, Vetter T (1996) Comparison of view-based object recognition algorithms using realistic 3D models. In: International joint conference on artificial intelligence, pp 251–256

    Chapter  Google Scholar 

  40. LeCun Y, Huang FJ, Bottou L (2004) Learning methods for generic object recognition with invariance to pose and lighting. In: IEEE conference on computer vision and pattern recognition, vol 2, pp 97–104

    Google Scholar 

  41. Liebelt J, Schmid C (2010) Multi-view object class detection with a 3D geometric model. In: IEEE conference on computer vision and pattern recognition, pp 1688–1695

    Google Scholar 

  42. Moore S, Bowden R (2011) Local binary patterns for multi-view facial expression recognition. Elsevier Comput Vis Image Underst 115(4):541–558

    Article  Google Scholar 

  43. Juefei-Xu F, Pal DK, Savvides M (2015) NIR-VIS heterogeneous face recognition via cross-spectral joint dictionary learning and reconstruction. In: IEEE conference on computer vision and pattern recognition, pp 141–150

    Google Scholar 

  44. Wang J, Wang G, Zhou M (2018) Bimodal vein data mining via cross-selected-domain knowledge transfer. IEEE Trans Inf Forensics Secur 13(3):733–744

    Article  Google Scholar 

  45. Dai W, Yang Q, Xue GR, Yu Y (2008) Self-taught clustering. In: International conference on machine learning, pp 200–207

    Google Scholar 

  46. Wang Z, Song Y, Zhang C (2008) Transferred dimensionality reduction. In: Joint European conference on machine learning and knowledge discovery in databases, pp 550–565

    Google Scholar 

  47. Du B, Zhang L, Tao D, Zhang D (2013) Unsupervised transfer learning for target detection from hyperspectral images. Elsevier Neurocomputing 120:72–82

    Article  Google Scholar 

  48. Peng P, Xiang T, Wang Y, Pontil M, Gong S, Huang T, Tian Y (2016) Unsupervised cross-dataset transfer learning for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 1306–1315

    Google Scholar 

  49. Pinheiro PO, Element A (2017) Unsupervised domain adaptation with similarity learning. In: IEEE conference on computer vision and pattern recognition, pp 8004–8013

    Google Scholar 

  50. Yang B, Ma AJ, Yuen PC (2018) Learning domain-shared group-sparse representation for unsupervised domain adaptation. Elsevier Pattern Recognit 81:615–632

    Article  Google Scholar 

  51. Alirezazadeh P, Hejrati B, Monsef-Esfehani A, Fathi A (2018) Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images. Elsevier Biocyber Biomed Eng 38(3):671–683

    Article  Google Scholar 

  52. Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The Caltech-UCSD birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology

    Google Scholar 

  53. Krause J, Stark M, Deng J, Fei-Fei L (2013) 3D object representations for fine-grained categorization. In: IEEE workshop on 3D representation and recognition (3dRR-13), Sydney, Australia

    Google Scholar 

  54. Peng X, Hoffman J, Yu SX, Saenko K (2016) Fine-to-coarse knowledge transfer for low-res image classification. arXiv:1605.06695

  55. Yao Y, Li X, Ye Y, Liu F, Ng MK, Huang Z, Zhang Y (2018) Low-resolution image categorization via heterogeneous domain adaptation. Knowl Based Syst 163:656–665

    Article  Google Scholar 

  56. Hu J, Lu J, Tan YP (2015) Deep transfer metric learning. In: IEEE conference on computer vision and pattern recognition, pp 325–333

    Google Scholar 

  57. Wang X, Duan X, Bai X (2016) Deep sketch feature for cross-domain image retrieval. Elsevier Neurocomputing 207:387–397

    Article  Google Scholar 

  58. Mittal P, Vatsa M, Singh R (2015) Composite sketch recognition via deep network-a transfer learning approach. In: IAPR international conference on biometrics, pp 251–256

    Google Scholar 

  59. Liu X, Song L, Wu X, Tan T (2016) Transferring deep representation for NIR-VIS heterogeneous face recognition. In: IAPR international conference on biometrics, pp 1–8

    Google Scholar 

  60. Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv:1503.02531

  61. Tzeng E, Hoffman J, Darrell T, Saenko K (2015) Simultaneous deep transfer across domains and tasks. In: IEEE international conference on computer vision, pp 4068–4076

    Google Scholar 

  62. Gebru T, Hoffman J, Fei-Fei L (2017) Fine-grained recognition in the wild: a multi-task domain adaptation approach. In: International conference on computer vision, pp 1358–1367

    Google Scholar 

  63. Motiian S, Piccirilli M, Adjeroh DA, Doretto G (2017) Unified deep supervised domain adaptation and generalization. In: IEEE international conference on computer vision, pp 5715–5725

    Google Scholar 

  64. Duan L, Xu D, Tsang I (2012) Learning with augmented features for heterogeneous domain adaptation. arXiv:1206.4660

  65. Wang C, Mahadevan S (2011) Heterogeneous domain adaptation using manifold alignment. In: International joint conference on artificial intelligence, vol 22, pp 1541–1546

    Chapter  Google Scholar 

  66. Zhou JT, Tsang IW, Pan SJ, Tan M (2014) Heterogeneous domain adaptation for multiple classes. In: Artificial intelligence and statistics, pp 1095–1103

    Google Scholar 

  67. Kulis B, Saenko K, Darrell T (2011) What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: IEEE conference on computer vision and pattern recognition, pp 1785–1792

    Google Scholar 

  68. Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision, pp 213–226

    Chapter  Google Scholar 

  69. Jhuo IH, Liu D, Lee D, Chang SF (2012) Robust visual domain adaptation with low-rank reconstruction. In: IEEE conference on computer vision and pattern recognition, pp 2168–2175

    Google Scholar 

  70. Tan B, Song Y, Zhong E, Yang Q (2015) Transitive transfer learning. In: International conference on knowledge discovery and data mining, pp 1155–1164

    Google Scholar 

  71. Tan B, Zhang Y, Pan SJ, Yang Q (2017) Distant domain transfer learning. In: AAAI conference on artificial intelligence, pp 2604–2610

    Google Scholar 

  72. Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M (2013) Unsupervised domain adaptation by domain invariant projection. In: IEEE international conference on computer vision, pp 769–776

    Google Scholar 

  73. Long M, Zhu H, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. In: Advances in neural information processing systems, pp 136–144

    Google Scholar 

  74. Zhang X, Yu FX, Chang SF, Wang S (2015) Deep transfer network: unsupervised domain adaptation. arXiv:1503.00591

  75. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: IEEE conference on computer vision and pattern recognition, pp 3722–3731

    Google Scholar 

  76. Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Advances in neural information processing systems, pp 469–477

    Google Scholar 

  77. Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: International conference on computer vision, pp 1125–1134

    Google Scholar 

  78. Yi Z, Zhang HR, Tan P, Gong M (2017) Dualgan: unsupervised dual learning for image-to-image translation. In: International conference on computer vision, pp 2868–2876

    Google Scholar 

  79. Tzeng E, Devin C, Hoffman J, Finn C, Abbeel P, Levine S, Saenko K, Darrell T (2015) Adapting deep visuomotor representations with weak pairwise constraints. arXiv:1511.07111

  80. Ganin Y, Lempitsky V (2014) Unsupervised domain adaptation by backpropagation. arXiv:1409.7495

  81. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2130

    MathSciNet  MATH  Google Scholar 

  82. Wang M, Deng W (2018) Deep visual domain adaptation: a survey. Neurocomputing 312:135–153

    Article  Google Scholar 

  83. Xie M, Jean N, Burke M, Lobell D, Ermon S (2015) Transfer learning from deep features for remote sensing and poverty mapping. arXiv:1510.00098

  84. Rusu AA, Rabinowitz NC, Desjardins G, Soyer H, Kirkpatrick J, Kavukcuoglu K, Pascanu R, Hadsell R (2016) Progressive neural networks. arXiv:1606.04671

  85. Csurka G (2017) Domain adaptation for visual applications: a comprehensive survey. arXiv:1702.05374

  86. Patel VM, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: a survey of recent advances. IEEE Signal Process Mag 32(3):53–69

    Article  Google Scholar 

  87. Shao L, Zhu F, Li X (2015) Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 26(5):1019–1034

    Article  MathSciNet  Google Scholar 

  88. Zhang J, Li W, Ogunbona P (2017) Transfer learning for cross-dataset recognition: a survey. arXiv:1705.04396

  89. Zhang L (2019) Transfer adaptation learning: a decade survey. arXiv:1903.04687

  90. Heckman J et al (2013) Sample selection bias as a specification error. Appl Econ 31(3):129–137

    Google Scholar 

  91. Zadrozny B (2004) Learning and evaluating classifiers under sample selection bias. In: International conference on machine learning, p 114–122

    Google Scholar 

  92. Jiang J (2008) Domain adaptation in natural language processing. Technical report

    Google Scholar 

  93. Zhao H, Yuen PC (2008) Incremental linear discriminant analysis for face recognition. IEEE Trans Syst Man Cybern Part B (Cybernetics) 38(1):210–221

    Article  Google Scholar 

  94. Liu LP, Jiang Y, Zhou ZH (2009) Least square incremental linear discriminant analysis. In: IEEE international conference on data mining, pp 298–306

    Google Scholar 

  95. Xiao T, Zhang J, Yang K, Peng Y, Zhang Z (2014) Error-driven incremental learning in deep convolutional neural network for large-scale image classification. In: ACM international conference on multimedia, pp 177–186

    Google Scholar 

  96. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A et al (2017) Overcoming catastrophic forgetting in neural networks. In: Proceedings of the national academy of sciences, vol 114(13), pp 3521–3526

    Article  MathSciNet  Google Scholar 

  97. Bhatt HS, Bharadwaj S, Singh R, Vatsa M, Noore A, Ross A (2011) On co-training online biometric classifiers. In: International joint conference on biometrics, pp 1–7

    Google Scholar 

  98. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Annual conference on computational learning theory, pp 92–100

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IIIT Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India

    Soumyadeep Ghosh, Richa Singh & Mayank Vatsa

  2. IBM TJ Watson Research Center, 1101 Kitchawan Road, Yorktown, NY, 10598, USA

    Nalini Ratha

  3. Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218-2625, USA

    Vishal M. Patel

Authors
  1. Soumyadeep Ghosh
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  2. Richa Singh
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  3. Mayank Vatsa
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  4. Nalini Ratha
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  5. Vishal M. Patel
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Correspondence to Richa Singh .

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Editors and Affiliations

  1. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Richa Singh

  2. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Mayank Vatsa

  3. Johns Hopkins University, Baltimore, MD, USA

    Vishal M. Patel

  4. IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Nalini Ratha

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Ghosh, S., Singh, R., Vatsa, M., Ratha, N., Patel, V.M. (2020). Domain Adaptation for Visual Understanding. In: Singh, R., Vatsa, M., Patel, V., Ratha, N. (eds) Domain Adaptation for Visual Understanding. Springer, Cham. https://doi.org/10.1007/978-3-030-30671-7_1

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