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Parameter-efficient transfer learning (PETL) based on large-scale pre-trained foundation models has achieved great success in various downstream applications.
Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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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
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Novel dataset for fine-grained image categorization: Stanford dogs
Khosla, A., Jayadevaprakash, N., Yao, B., and Li, F.-F · 2011
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The Caltech-UCSD birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., and Belongie, S · 2015
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Fine-grained car detection for visual census estimation
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., and Fei-Fei, L · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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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
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Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2018
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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
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VisualBERT: A simple and performant baseline for vision and language
Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., and Chang, K.-W · 2019
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RoBERTa: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
End-to-end object detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S · 2020
Cited alongside, same era.
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., et al · 2020
Cited alongside, same era.
Parameter-efficient transfer learning with diff pruning
Guo, D., Rush, A. M., and Kim, Y · 2020
Cited alongside, same era.
AdapterFusion: Non-destructive task composition for transfer learning
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., and Gurevych, I · 2020
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
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Pyramid vision Transformer: A versatile backbone for dense prediction without convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L · 2021
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BitFit: Simple parameter-efficient fine-tuning for Transformerbased masked language-models
Zaken, E. B., Ravfogel, S., and Goldberg, Y · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with Transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H., and Zhang, L · 2021
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Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
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Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
BEiT: BERT pre-training of image Transformers
Bao, H., Dong, L., Piao, S., and Wei, F · 2021
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Hu, E., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, L., and Chen, W · 2021
Cited alongside, same era.
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
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
Cited alongside, same era.
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AdaptFormer: Adapting vision Transformers for scalable visual recognition
Chen, S., Ge, C., Tong, Z., Wang, J., Song, Y., Wang, J., and Luo, P · 2022
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Visual prompt tuning
Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., and Lim, S.-N · 2022
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UniFormer: Unified Transformer for efficient spatiotemporal representation learning
Li, K., Wang, Y., Gao, P., Song, G., Liu, Y., Li, H., and Qiao, Y · 2022
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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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ViDT: An efficient and effective fully Transformer-based object detector
Song, H., Sun, D., Chun, S., Jampani, V., Han, D., Heo, B., Kim, W., and Yang, M.-H · 2022
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CoCa: Contrastive captioners are image-text foundation models
Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., and Wu, Y · 2022
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Zhang, Y., Zhou, K., and Liu, Z · 2022
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