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Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning.
Language models are few-shot learners
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A simple semi-supervised learning framework for object detection
Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., and Pfister, T. (2020) · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013) · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014) · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T. (2014) · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014) · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S. (2014) · 2014
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Two-stream convolutional networks for action recognition in videos
Simonyan, K. and Zisserman, A. (2014) · 2014
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T. (2015) · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2015) · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016) · 2016
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Universal representations: The missing link between faces, text, planktons, and cat breeds
Bilen, H. and Vedaldi, A. (2017) · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
Carreira, J. and Zisserman, A. (2017) · 2017
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017) · 2017
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The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al. (2017) · 2017
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Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017) · 2017
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Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H., and Vedaldi, A. (2017) · 2017
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Cascade r-cnn: Delving into high quality object detection
Cai, Z. and Vasconcelos, N. (2018) · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018) · 2018
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Masked-attention mask transformer for universal image segmentation
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., and Girdhar, R. (2021) · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2021) · 2021
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Simple copy-paste is a strong data augmentation method for instance segmentation
Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.-Y., Cubuk, E. D., Le, Q. V., and Zoph, B. (2021) · 2021
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. (2021) · 2021
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Efficient parametrization of multi-domain deep neural networks
Rebuffi, S.-A., Bilen, H., and Vedaldi, A. (2018) · 2018
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Unified perceptual parsing for scene understanding
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018) · 2018
Cited alongside, same era.
Semantic understanding of scenes through the ade20k dataset
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., and Torralba, A. (2018) · 2018
Cited alongside, same era.
Hybrid task cascade for instance segmentation
Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., et al. (2019) · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S. (2019) · 2019
Cited alongside, same era.
Big transfer (bit): General visual representation learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2019) · 2019
Cited alongside, same era.
Parametric instance classification for unsupervised visual feature learning
Cao, Y., Xie, Z., Liu, B., Lin, Y., Zhang, Z., and Hu, H. (2020) · 2020
Cited alongside, same era.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021) · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T. (2021) · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N. (2021) · 2021
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Efficient self-supervised vision transformers for representation learning
Li, C., Yang, J., Zhang, P., Gao, M., Xiao, B., Dai, X., Yuan, L., and Gao, J. (2021) · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P. (2021) · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., and Komatsuzaki, A. (2021) · 2021
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Simmim: A simple framework for masked image modeling
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., and Hu, H. (2021) · 2021
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End-to-end semi-supervised object detection with soft teacher
Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., Bai, X., and Liu, Z. (2021) · 2021
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Cpt: Colorful prompt tuning for pre-trained vision-language models
Yao, Y., Zhang, A., Zhang, Z., Liu, Z., Chua, T.-S., and Sun, M. (2021) · 2021
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Learning to prompt for vision-language models
Zhou, K., Yang, J., Loy, C. C., and Liu, Z. (2021) · 2021
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Proper reuse of image classification features improves object detection
Vasconcelos, C., Birodkar, V., and Dumoulin, V. (2022) · 2022
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icar: Bridging image classification and image-text alignment for visual recognition
Wei, Y., Cao, Y., Zhang, Z., Yao, Z., Xie, Z., Hu, H., and Guo, B. (2022) · 2022
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