Abstract
Domain generalization (DG) aims to transfer the knowledge learned in the source domain to the unseen target domain. Most DG methods focus on studying how to learn domain-invariant representations that remain invariant across different domains. For humans, we tend to use the same word or text to describe images from different domains but of the same category. Therefore, text can be considered a natural domain-invariant representation. Inspired by this, this paper studies how to introduce text representations into domain generalization tasks. Specifically, the text representations generated by CLIP text encoder are used to guide the image representation learning of the visual model. To alleviate domain bias and weak discriminability caused by CLIP representations, a joint loss is proposed by combining the text representation regularization loss with standard image-level supervised loss. The proposed method is simple yet efficient, and can achieve competitive performance compared with the existing state-of-the-art methods on five standard DG datasets.
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References
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
Bui, M.H., Tran, T., Tran, A., Phung, D.: Exploiting domain-specific features to enhance domain generalization. In: Advances in Neural Information Processing Systems, vol. 34, pp. 21189–21201 (2021)
Cha, J., et al.: Swad: domain generalization by seeking flat minima. In: Advances in Neural Information Processing Systems, vol. 34, pp. 22405–22418 (2021)
Cha, J., Lee, K., Park, S., Chun, S.: Domain generalization by mutual-information regularization with pre-trained models. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13683, pp. 440–457. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20050-2_26
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)
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)
Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. In: International Conference on Learning Representations (2020)
Kim, D., Yoo, Y., Park, S., Kim, J., Lee, J.: Selfreg: self-supervised contrastive regularization for domain generalization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9619–9628 (2021)
Krueger, D., et al.: Out-of-distribution generalization via risk extrapolation (rex). In: International Conference on Machine Learning, pp. 5815–5826. PMLR (2021)
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)
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)
Li, L., et al.: Progressive domain expansion network for single domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 224–233 (2021)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
Min, S., Park, N., Kim, S., Park, S., Kim, J.: Grounding visual representations with texts for domain generalization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13697, pp. 37–53. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19836-6_3
Nam, H., Lee, H., Park, J., Yoon, W., Yoo, D.: Reducing domain gap by reducing style bias. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8690–8699 (2021)
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)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211–252 (2015)
Shi, Y., et al.: Gradient matching for domain generalization. arXiv preprint arXiv:2104.09937 (2021)
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
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1999). https://doi.org/10.1007/978-1-4757-3264-1
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)
Wang, Z., Wu, Z., Agarwal, D., Sun, J.: Medclip: contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022)
Yao, X., et al.: PCL: proxy-based contrastive learning for domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7097–7107 (2022)
Zhang, M., Marklund, H., Dhawan, N., Gupta, A., Levine, S., Finn, C.: Adaptive risk minimization: a meta-learning approach for tackling group distribution shift (2020)
Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle. In: International Conference on Learning Representations (2020)
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (Grant Nos. 62373324, 62271448 and U20A20171), in part by Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LGF22F030016 and LY21F020027), and in part Key Programs for Science and Technology Development of Zhejiang Province (2022C03113).
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Zhang, H., Hu, H., Chen, Q., Zhou, Q., Jiang, M. (2024). Learning Domain-Invariant Representations from Text for Domain Generalization. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14432. Springer, Singapore. https://doi.org/10.1007/978-981-99-8543-2_10
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DOI: https://doi.org/10.1007/978-981-99-8543-2_10
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