[1] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. URL https://www.tensorflow.org/. Software available from tensorflow.org.
[2] Bain, M., Nagrani, A., Varol, G., and Zisserman, A. Frozen in time: A joint video and image encoder for end-to-end retrieval. In International Conference on Computer Vision (ICCV), 2021. URL https://arxiv.org/abs/2104.00650.
[3] Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gufreund, D., Tenenbaum, J., and Katz, B. Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. In Advances in Neural Information Processing Systems (NeurIPS), 2019. URL https://proceedings.neurips.cc/paper/2019/file/97af07a14cacba681feacf3012730892-Paper.pdf.
[4] Beyer, L., Zhai, X., and Kolesnikov, A. Big vision. https://github.com/google-research/big_vision, 2022.
[5] Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q. JAX: composable transformations of Python+NumPy programs, 2018. URL http://github.com/google/jax.
[6] Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A. Emerging properties in self-supervised vision transformers. In International conference on computer vision (ICCV), 2021. URL https://arxiv.org/abs/2104.14294.
[7] Changpinyo, S., Sharma, P., Ding, N., and Soricut, R. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Conference on Computer Vision and Pattern Recognition (CVPR), 2021. URL https://arxiv.org/abs/2102.08981.
[8] Chen, J., Hu, H., Wu, H., Jiang, Y., and Wang, C. Learning the best pooling strategy for visual semantic embedding. In Conference on computer vision and pattern recognition (CVPR), 2021. URL https://arxiv.org/abs/2011.04305.
[9] Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. A simple framework for contrastive learning of visual representations. In International conference on machine learning (ICML), 2020. URL https://arxiv.org/abs/2002.05709.
[10] Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., and Zitnick, C. L. Microsoft coco captions: Data collection and evaluation server, 2015. URL https://arxiv.org/pdf/1504.00325.pdf.
[11] Chen, X., Wang, X., Changpinyo, S., Piergiovanni, A., Padlewski, P., Salz, D., Goodman, S., Gryncer, A., Mustafa, B., Beyer, L., et al. Pali: A jointly-scaled multilingual language-image model, 2022. URL https://arxiv.org/abs/2209.06794.
[12] Cheng, G., Han, J., and Lu, X. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 2017. URL https://arxiv.org/abs/1703.00121.
[13] Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A. Describing textures in the wild. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014. URL https://arxiv.org/abs/1311.3618.
[14] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. Imagenet: A large-scale hierarchical image database. In Conference on Computer Vision and Pattern Recognition (CVPR), 2009. URL https://ieeexplore.ieee.org/document/5206848.
[15] Djolonga, J., Hubis, F., Minderer, M., Nado, Z., Nixon, J., Romijnders, R., Tran, D., and Lucic, M. Robustness Metrics, 2020. URL https://github.com/google-research/robustness_metric.
[16] Doersch, C., Gupta, A., and Efros, A. A. Unsupervised visual representation learning by context prediction. In International conference on computer vision (ICCV), 2015. URL https://arxiv.org/abs/1505.05192.