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Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks.
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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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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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
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Deep neural networks: a new framework for modelling biological vision and brain information processing
Kriegeskorte, N · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Deep learning in neural networks: An overview
Schmidhuber, J · 2015
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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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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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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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Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S · 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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Scene parsing through ade20k dataset
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A · 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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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 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
Cited alongside, same era.
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
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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An empirical study of training self-supervised vision transformers
Chen, X., Xie, S., and He, K · 2021
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Warp: Word-level adversarial reprogramming
Hambardzumyan, K., Khachatrian, H., and May, J · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
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Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., et al · 2019
Cited alongside, same era.
Tinytl: Reduce memory, not parameters for efficient on-device learning
Cai, H., Gan, C., Zhu, L., and Han, S · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 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.
How does selective mechanism improve self-attention networks?
Geng, X., Wang, L., Wang, X., Qin, B., Liu, T., and Tu, Z · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Cited alongside, same era.
Learning how to ask: Querying lms with mixtures of soft prompts
Qin, G. and Eisner, J · 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., et al · 2021
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ibot: Image bert pre-training with online tokenizer
Zhou, J., Wei, C., Wang, H., Shen, W., Xie, C., Yuille, A., and Kong, T · 2021
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Visual prompting: Modifying pixel space to adapt pre-trained models
Bahng, H., Jahanian, A., Sankaranarayanan, S., and Isola, P · 2022
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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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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., and Lim, S.-N · 2022
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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 · 2022
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An extensible plug-and-play method for multi-aspect controllable text generation
Huang, X., Liu, Z., Li, P., Li, T., Sun, M., and Liu, Y · 2023
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