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NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

NICO challenge of out-of-distribution (OOD) generalization for image recognition features two tracks: common context generalization and hybrid context generalization, based on a newly proposed OOD dataset called NICO\(^{++}\). Strong distribution shifts between the training and test data are constructed for both tracks. In contrast to the current OOD generalization benchmarks where models are tested on a single domain, NICO challenge tests models on multiple domains for a thorough and comprehensive evaluation. To prevent the leakage of target context knowledge and encourage novel and creative solutions instead of leveraging additional training data, we prohibit the model initialization with pretrained parameters, which is not noticed in the previous benchmarks for OOD generalization. To ensure the random initialization of models, we verify and retrain models from all top-10 teams and test them on the private test data. We empirically show that pretraining on ImageNet introduces considerable bias on test performance. We summarize the insights in top-4 solutions, which outperform the official baselines significantly, and the approach of jury award for each track.

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Notes

  1. 1.

    We use the two words domain and context interchangeably.

  2. 2.

    https://nicochallenge.com/.

References

  1. Alcorn, M.A., et al.: Strike (with) a pose: neural networks are easily fooled by strange poses of familiar objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4845–4854 (2019)

    Google Scholar 

  2. Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)

  3. Balaji, Y., Sankaranarayanan, S., Chellappa, R.: MetaReg: towards domain generalization using meta-regularization. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  4. Bandi, P., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE Trans. Med. Imaging 38(2), 550–560 (2018)

    Article  Google Scholar 

  5. Beery, S., Agarwal, A., Cole, E., Birodkar, V.: The iwildcam 2021 competition dataset. arXiv preprint arXiv:2105.03494 (2021)

  6. Beery, S., Van Horn, G., Perona, P.: Recognition in terra incognita. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 472–489. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_28

    Chapter  Google Scholar 

  7. Berman, D.S., Buczak, A.L., Chavis, J.S., Corbett, C.L.: A survey of deep learning methods for cyber security. Information 10(4), 122 (2019)

    Article  Google Scholar 

  8. Blanchard, G., Deshmukh, A.A., Dogan, Ü., Lee, G., Scott, C.: Domain generalization by marginal transfer learning. J. Mach. Learn. Res. 22(1), 46–100 (2021)

    MathSciNet  MATH  Google Scholar 

  9. Blanchard, G., Lee, G., Scott, C.: Generalizing from several related classification tasks to a new unlabeled sample. NeurIPS 24, 2178–2186 (2011)

    Google Scholar 

  10. Brock, A., De, S., Smith, S.L., Simonyan, K.: High-performance large-scale image recognition without normalization. In: International Conference on Machine Learning, pp. 1059–1071. PMLR (2021)

    Google Scholar 

  11. Castro, D.C., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11(1), 1–10 (2020)

    Article  Google Scholar 

  12. Christie, G., Fendley, N., Wilson, J., Mukherjee, R.: Functional map of the world. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6172–6180 (2018)

    Google Scholar 

  13. Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)

  14. Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702–703 (2020)

    Google Scholar 

  15. Dai, D., Van Gool, L.: Dark model adaptation: semantic image segmentation from daytime to nighttime. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3819–3824. IEEE (2018)

    Google Scholar 

  16. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  17. Ding, Z., Fu, Y.: Deep domain generalization with structured low-rank constraint. IEEE Trans. Image Process. 27(1), 304–313 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  18. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)

    Google Scholar 

  19. Du, J., Zhou, D., Feng, J., Tan, V.Y., Zhou, J.T.: Sharpness-aware training for free. arXiv preprint arXiv:2205.14083 (2022)

  20. D’Innocente, A., Caputo, B.: Domain generalization with domain-specific aggregation modules. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 187–198. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12939-2_14

    Chapter  Google Scholar 

  21. Fang, C., Xu, Y., Rockmore, D.N.: Unbiased metric learning: on the utilization of multiple datasets and web images for softening bias. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1657–1664 (2013)

    Google Scholar 

  22. Garg, V., Kalai, A.T., Ligett, K., Wu, S.: Learn to expect the unexpected: probably approximately correct domain generalization. In: International Conference on Artificial Intelligence and Statistics, pp. 3574–3582. PMLR (2021)

    Google Scholar 

  23. Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D.: Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2551–2559 (2015)

    Google Scholar 

  24. Gong, R., Li, W., Chen, Y., Gool, L.V.: Dlow: Domain flow for adaptation and generalization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2477–2486 (2019)

    Google Scholar 

  25. Guo, M.H., Lu, C.Z., Liu, Z.N., Cheng, M.M., Hu, S.M.: Visual attention network. arXiv preprint arXiv:2202.09741 (2022)

  26. Han, D., Kim, J., Kim, J.: Deep pyramidal residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5927–5935 (2017)

    Google Scholar 

  27. Han, D., Yun, S., Heo, B., Yoo, Y.: Rethinking channel dimensions for efficient model design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 732–741 (2021)

    Google Scholar 

  28. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  29. He, Y., Shen, Z., Cui, P.: Towards non-IID image classification: a dataset and baselines. Pattern Recogn. 110, 107383 (2021)

    Article  Google Scholar 

  30. Hendrycks, D., et al.: The many faces of robustness: a critical analysis of out-of-distribution generalization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8340–8349 (2021)

    Google Scholar 

  31. Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261 (2019)

  32. Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: a simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781 (2019)

  33. Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15262–15271 (2021)

    Google Scholar 

  34. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  35. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  36. Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., Wilson, A.G.: Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 (2018)

  37. Jang, W.D., et al.: Learning vector quantized shape code for amodal blastomere instance segmentation. arXiv preprint arXiv:2012.00985 (2020)

  38. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  39. Koh, P.W., et al.: Wilds: a benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning, pp. 5637–5664. PMLR (2021)

    Google Scholar 

  40. LeCun, Y.: The mnist database of handwritten digits (1998). http://yann.lecun.com/exdb/mnist/

  41. Lee, Y., Kim, J., Willette, J., Hwang, S.J.: Mpvit: multi-path vision transformer for dense prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7287–7296 (2022)

    Google Scholar 

  42. Levinson, J., et al.: Towards fully autonomous driving: systems and algorithms. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 163–168. IEEE (2011)

    Google Scholar 

  43. Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5542–5550 (2017)

    Google Scholar 

  44. Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Learning to generalize: meta-learning for domain generalization. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  45. Li, H., Pan, S.J., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5400–5409 (2018)

    Google Scholar 

  46. Li, K., et al.: Uniformer: unifying convolution and self-attention for visual recognition. arXiv preprint arXiv:2201.09450 (2022)

  47. Li, Y., Yang, Y., Zhou, W., Hospedales, T.: Feature-critic networks for heterogeneous domain generalization. In: International Conference on Machine Learning, pp. 3915–3924. PMLR (2019)

    Google Scholar 

  48. Liao, Y., Huang, R., Li, J., Chen, Z., Li, W.: Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed. IEEE Trans. Instrum. Meas. 69(10), 8064–8075 (2020)

    Google Scholar 

  49. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)

    Google Scholar 

  50. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 19(6), 1236–1246 (2018)

    Article  Google Scholar 

  51. Muandet, K., Balduzzi, D., Schölkopf, B.: Domain generalization via invariant feature representation. In: ICML, pp. 10–18. PMLR (2013)

    Google Scholar 

  52. Pei, S., Sun, J., Xiang, S., Meng, G.: Domain decorrelation with potential energy ranking. arXiv e-prints pp. arXiv-2207 (2022)

    Google Scholar 

  53. Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1406–1415 (2019)

    Google Scholar 

  54. Peng, X.B., Andrychowicz, M., Zaremba, W., Abbeel, P.: Sim-to-real transfer of robotic control with dynamics randomization. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3803–3810. IEEE (2018)

    Google Scholar 

  55. Prakash, A., et al.: Structured domain randomization: bridging the reality gap by context-aware synthetic data. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 7249–7255. IEEE (2019)

    Google Scholar 

  56. Qiao, F., Zhao, L., Peng, X.: Learning to learn single domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12556–12565 (2020)

    Google Scholar 

  57. Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P.: Designing network design spaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428–10436 (2020)

    Google Scholar 

  58. Ridnik, T., Lawen, H., Noy, A., Ben Baruch, E., Sharir, G., Friedman, I.: Tresnet: high performance GPU-dedicated architecture. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1400–1409 (2021)

    Google Scholar 

  59. Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731 (2019)

  60. Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., Sarawagi, S.: Generalizing across domains via cross-gradient training. arXiv preprint arXiv:1804.10745 (2018)

  61. Shen, Z., et al.: Towards out-of-distribution generalization: a survey. arXiv preprint arXiv:2108.13624 (2021)

  62. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  63. Somavarapu, N., Ma, C.Y., Kira, Z.: Frustratingly simple domain generalization via image stylization. arXiv preprint arXiv:2006.11207 (2020)

  64. Sun, B., Feng, J., Saenko, K.: Return of frustratingly easy domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

  65. Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_35

    Chapter  Google Scholar 

  66. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  67. Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

  68. Tan, M., Le, Q.V.: Mixconv: mixed depthwise convolutional kernels. arXiv preprint arXiv:1907.09595 (2019)

  69. Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)

  70. Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018–5027 (2017)

    Google Scholar 

  71. Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  72. Wang, J., Chen, Y., Feng, W., Yu, H., Huang, M., Yang, Q.: Transfer learning with dynamic distribution adaptation. ACM Trans. Intell. Syst. Technol. (TIST) 11(1), 1–25 (2020)

    Google Scholar 

  73. Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 402–410 (2018)

    Google Scholar 

  74. Wang, J., Lan, C., Liu, C., Ouyang, Y., Zeng, W., Qin, T.: Generalizing to unseen domains: a survey on domain generalization. arXiv preprint arXiv:2103.03097 (2021)

  75. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-net: efficient channel attention for deep convolutional neural networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020)

    Google Scholar 

  76. Wang, S., Yu, L., Li, K., Yang, X., Fu, C.W., Heng, P.A.: Dofe: domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets. IEEE TMI 39(12), 4237–4248 (2020)

    Google Scholar 

  77. Wang, T., Zhou, C., Sun, Q., Zhang, H.: Causal attention for unbiased visual recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3091–3100 (2021)

    Google Scholar 

  78. Xu, R., Zhang, X., Cui, P., Li, B., Shen, Z., Xu, J.: Regulatory instruments for fair personalized pricing. In: Proceedings of the ACM Web Conference 2022, pp. 4–15 (2022)

    Google Scholar 

  79. Xu, R., Zhang, X., Shen, Z., Zhang, T., Cui, P.: A theoretical analysis on independence-driven importance weighting for covariate-shift generalization. In: International Conference on Machine Learning, pp. 24803–24829. PMLR (2022)

    Google Scholar 

  80. Yang, Y., Soatto, S.: FDA: Fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085–4095 (2020)

    Google Scholar 

  81. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)

    Google Scholar 

  82. Yuan, L., Hou, Q., Jiang, Z., Feng, J., Yan, S.: Volo: vision outlooker for visual recognition. arXiv preprint arXiv:2106.13112 (2021)

  83. Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., Gong, B.: Domain randomization and pyramid consistency: simulation-to-real generalization without accessing target domain data. In: ICCV, pp. 2100–2110 (2019)

    Google Scholar 

  84. Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)

    Google Scholar 

  85. Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)

  86. Zhang, H., et al.: Resnest: split-attention networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2736–2746 (2022)

    Google Scholar 

  87. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (2018)

    Google Scholar 

  88. Zhang, X., Cheng, F., Wang, S.: Spatio-temporal fusion based convolutional sequence learning for lip reading. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 713–722 (2019)

    Google Scholar 

  89. Zhang, X., Cui, P., Xu, R., Zhou, L., He, Y., Shen, Z.: Deep stable learning for out-of-distribution generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5372–5382 (2021)

    Google Scholar 

  90. Zhang, X., et al.: Towards domain generalization in object detection. arXiv preprint arXiv:2203.14387 (2022)

  91. Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., Liu, H.: Nico++: towards better benchmarking for domain generalization. arXiv preprint arXiv:2204.08040 (2022)

  92. Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., Liu, H.: Towards unsupervised domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4910–4920 (2022)

    Google Scholar 

  93. Zhang, Y., Li, M., Li, R., Jia, K., Zhang, L.: Exact feature distribution matching for arbitrary style transfer and domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8035–8045 (2022)

    Google Scholar 

  94. Zhou, D., et al.: Understanding the robustness in vision transformers. In: International Conference on Machine Learning, pp. 27378–27394. PMLR (2022)

    Google Scholar 

  95. Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Change Loy, C.: Domain generalization: a survey. arXiv e-prints pp. arXiv-2103 (2021)

    Google Scholar 

  96. Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Deep domain-adversarial image generation for domain generalisation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13025–13032 (2020)

    Google Scholar 

  97. Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Learning to generate novel domains for domain generalization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 561–578. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_33

    Chapter  Google Scholar 

  98. Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008 (2021)

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Acknowledgement

This work was supported in part by National Key R &D Program of China (No. 2018AAA0102004, No. 2020AAA0106300), National Natural Science Foundation of China (No. U1936219, 61521002, 61772304), Beijing Academy of Artificial Intelligence (BAAI), and a grant from the Institute for Guo Qiang, Tsinghua University.

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Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Xingxuan Zhang, Yue He, Han Yu, Zimu Wang, Jie Peng, Renzhe Xu, Zheyan Shen & Peng Cui

  2. Nanyang Technological University, Singapore, Singapore

    Tan Wang, Jiaxin Qi & Hanwang Zhang

  3. Columbia University, New York, USA

    Yulei Niu

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  1. Xingxuan Zhang
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  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Zhang, X. et al. (2023). NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13806. Springer, Cham. https://doi.org/10.1007/978-3-031-25075-0_29

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