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We propose a novel framework for few-shot learning by leveraging large-scale vision-language models such as CLIP.
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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in European Conference on Computer Vision (ECCV) . Springer, 2014, pp. 740–755
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2014
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L. Bossard, M. Guillaumin, and L. Van Gool, “Food-101–mining discriminative components with random forests,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13 . Springer, 2014, pp. 446–461
2014
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2015
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O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 29, 2016
2016
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R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li et al. , “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” International Journal of Computer Vision (IJCV) , vol. 123, no. 1, pp. 32–73, 2017
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C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International Conference on Machine Learning (ICML) , 2017, pp. 1126–1135
2017
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Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola, “Rethinking few-shot image classification: a good embedding is all you need?” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 266–282
2020
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C. Doersch, A. Gupta, and A. Zisserman, “Crosstransformers: spatially-aware few-shot transfer,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 21 981–21 993, 2020
2020
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning , 2021, pp. 8748–8763
2021
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P. Gao, S. Geng, R. Zhang, T. Ma, R. Fang, Y. Zhang, H. Li, and Y. Qiao, “Clip-adapter: Better vision-language models with feature adapters,” arXiv 2110.04544 , 2021
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J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 30, 2017
2017
Cited alongside, same era.
S. Gidaris and N. Komodakis, “Dynamic few-shot visual learning without forgetting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 4367–4375
2018
Cited alongside, same era.
H. Qi, M. Brown, and D. G. Lowe, “Low-shot learning with imprinted weights,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 5822–5830
2018
Cited alongside, same era.
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1199–1208
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 12, no. 7, pp. 2217–2226, 2019
2019
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2021
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M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 438–13 444
2021
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K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Learning to prompt for vision-language models,” International Journal of Computer Vision , vol. 130, no. 9, pp. 2337–2348, 2022
2022
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2022
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2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
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2022
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2022
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J. J. P, Y.-W. Chao, and Y. Xiang, “Fewsol: A dataset for few-shot object learning in robotic environments,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 9140–9146
2023
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