Abstract
Recent work has shown that self-supervised pre-training leads to improvements over supervised learning on challenging visual recognition tasks. CLIP, an exciting new approach to learning with language supervision, demonstrates promising performance on a wide variety of benchmarks. In this work, we explore whether self-supervised learning can aid in the use of language supervision for visual representation learning with Vision Transformers. We introduce SLIP, a multi-task learning framework for combining self-supervised learning and CLIP pre-training. After pre-training, we thoroughly evaluate representation quality and compare performance to both CLIP and self-supervised learning under three distinct settings: zero-shot transfer, linear classification, and end-to-end finetuning. Across ImageNet and a battery of additional datasets, we find that SLIP improves accuracy by a large margin. We validate our results further with experiments on different model sizes, training schedules, and pre-training datasets. Our findings show that SLIP enjoys the best of both worlds: better performance than self-supervision (+8.1% linear accuracy) and language supervision (+5.2% zero-shot accuracy). Our code is available at: github.com/facebookresearch/SLIP.
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Notes
- 1.
The initial 7\(\times \)7 conv is replaced by three 3\(\times \)3 convs; global average pooling is replaced by a self-attention pooling layer with 14M parameters.
- 2.
This model achieves 88.4% top-1 accuracy on ImageNet.
References
Bao, H., Dong, L., Wei, F.: Beit: bert pre-training of image transformers. ArXiv abs/2106.08254 (2021)
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 139–156. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_9
Caron, M., et al.: Emerging properties in self-supervised vision transformers. ArXiv abs/2104.14294 (2021)
Changpinyo, S., Sharma, P.K., Ding, N., Soricut, R.: Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3557–3567 (2021)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. ArXiv abs/2002.05709 (2020)
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners. ArXiv abs/2006.10029 (2020)
Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. ArXiv abs/2104.02057 (2021)
Desai, K., Johnson, J.: Virtex: Learning visual representations from textual annotations. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11157–11168 (2021)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. ArXiv abs/2010.11929 (2021)
El-Nouby, A., Izacard, G., Touvron, H., Laptev, I., Jégou, H., Grave, E.: Are large-scale datasets necessary for self-supervised pre-training? ArXiv abs/2112.10740 (2021)
Frome, A., et al.: Devise: a deep visual-semantic embedding model. In: NIPS (2013)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Goyal, P., et al.: Vissl (2021). https://github.com/facebookresearch/vissl
Goyal, P., Mahajan, D.K., Gupta, A., Misra, I.: Scaling and benchmarking self-supervised visual representation learning. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6390–6399 (2019)
Grill, J.B., et al.: Bootstrap your own latent: A new approach to self-supervised learning. ArXiv abs/2006.07733 (2020)
He, K., Chen, X., Xie, S., Li, Y., Doll’ar, P., Girshick, R.B.: Masked autoencoders are scalable vision learners (2021)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.B.: Momentum contrast for unsupervised visual representation learning. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9726–9735 (2020)
Ilharco, G., et al.: Openclip (2021). https://doi.org/10.5281/zenodo.5143773
Jain, A., et al.: Mural: Multimodal, multitask retrieval across languages. ArXiv abs/2109.05125 (2021)
Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: ICML (2021)
Joulin, A., van der Maaten, L., Jabri, A., Vasilache, N.: Learning visual features from large weakly supervised data. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 67–84. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_5
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2012)
Li, A., Jabri, A., Joulin, A., van der Maaten, L.: Learning visual n-grams from web data. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4193–4202 (2017)
Li, Y., et al.: Supervision exists everywhere: a data efficient contrastive language-image pre-training paradigm. ArXiv abs/2110.05208 (2021)
van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. ArXiv abs/1807.03748 (2018)
Quattoni, A., Collins, M., Darrell, T.: Learning visual representations using images with captions. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)
Radford, A., Narasimhan, K.: Improving language understanding by generative pre-training (2018)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)
Sariyildiz, M.B., Perez, J., Larlus, D.: Learning visual representations with caption annotations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 153–170. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_10
Sharma, P., Ding, N., Goodman, S., Soricut, R.: Conceptual captions: a cleaned, hypernymed, image alt-text dataset for automatic image captioning. In: ACL (2018)
Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K.S., Poland, D.N., Borth, D., Li, L.J.: Yfcc100m: the new data in multimedia research. Commun. ACM 59, 64–73 (2016)
Tian, Y., Henaff, O.J., Oord, A.v.d.: Divide and contrast: Self-supervised learning from uncurated data. arXiv preprint arXiv:2105.08054 (2021)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR 2011, pp. 1521–1528 (2011)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J’egou, H.: Training data-efficient image transformers & distillation through attention. In: ICML (2021)
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance-level discrimination. ArXiv abs/1805.01978 (2018)
Yuan, X., et al.: Multimodal contrastive training for visual representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6991–7000 (2021)
Zhai, X., et al.: A large-scale study of representation learning with the visual task adaptation benchmark. arXiv: Computer Vision and Pattern Recognition (2019)
Acknowledgements
This work was supported by BAIR, the Berkeley Deep Drive (BDD) project, and gifts from Meta and Open Philanthropy.
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Mu, N., Kirillov, A., Wagner, D., Xie, S. (2022). SLIP: Self-supervision Meets Language-Image Pre-training. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13686. Springer, Cham. https://doi.org/10.1007/978-3-031-19809-0_30
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