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2025

Parameter-Efficient Fine-Tuning for Foundation Models

Zhang, Dan, Feng, Tao, Xue, Lilong et al.

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This survey delves into the realm of Parameter-Efficient Fine-Tuning (PEFT) within the context of Foundation Models (FMs).

PEFT, a cost-effective fine-tuning technique, minimizes parameters and computational complexity while striving for optimal downstream task performance.
FMs, like ChatGPT, DALL-E, and LLaVA specialize in language understanding, generative tasks, and multimodal tasks, trained on diverse datasets spanning text, images, and videos.
The diversity of FMs guides various adaptation strategies for PEFT.

Parameter-Efficient Fine-Tuning for Foundation Models · Around

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E. T. K. Sang and F. D. Meulder, “Introduction to the conll-2003 shared task: Language-independent named entity recognition,” in Conference on Computational Natural Language Learning , 2003
2003
Earlier work this paper cites.
K.-L. Liu, W.-J. Li, and M. Guo, “Emoticon smoothed language models for twitter sentiment analysis,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 26, no. 1, 2012, pp. 1678–1684
2012
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W.-K. Wong, and W. chun Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in NIPS , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision , 2016
2016
Earlier work this paper cites.
D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv: Learning , 2016
2016
Earlier work this paper cites.
Y. Li, Y. Liang, and A. Risteski, “Recovery guarantee of weighted low-rank approximation via alternating minimization,” in International Conference on Machine Learning , 2016
2016
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” arXiv preprint arXiv:1706.03762 , 2017
Original
2017
Earlier work this paper cites.
C. Louizos, M. Welling, and D. P. Kingma, “Learning sparse neural networks through l0 regularization,” ArXiv , vol. abs/1712.01312, 2017
Original
2017
Earlier work this paper cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning multiple visual domains with residual adapters,” in NIPS , 2017
2017
Earlier work this paper cites.
J. Novikova, O. Dusek, and V. Rieser, “The e2e dataset: New challenges for end-to-end generation,” ArXiv , vol. abs/1706.09254, 2017
Original
2017
Earlier work this paper cites.
C. Gardent, A. Shimorina, S. Narayan, and L. Perez-Beltrachini, “The webnlg challenge: Generating text from rdf data,” in International Conference on Natural Language Generation , 2017
2017
Earlier work this paper cites.
Y. Li, T. Ma, and H. Zhang, “Algorithmic regularization in over-parameterized matrix sensing and neural networks with quadratic activations,” in Annual Conference Computational Learning Theory , 2017
2017
Earlier work this paper cites.
A. Radford, “Improving language understanding by generative pre-training,” OpenAI , 2018
2018
Earlier work this paper cites.
J. Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
X. Liu, Q. Chen, C. Deng, H.-J. Zeng, J. Chen, D. Li, and B. Tang, “Lcqmc:a large-scale chinese question matching corpus,” in International Conference on Computational Linguistics , 2018
2018
Earlier work this paper cites.
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman, “Glue: A multi-task benchmark and analysis platform for natural language understanding,” in BlackboxNLP@EMNLP , 2018
2018
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
Y. Liu and M. Lapata, “Text summarization with pretrained encoders,” arXiv preprint arXiv:1908.08345 , 2019
Original
2019
Earlier work this paper cites.
A. Conneau and G. Lample, “Cross-lingual language model pretraining,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
J. Lee, R. Tang, and J. J. Lin, “What would elsa do? freezing layers during transformer fine-tuning,” ArXiv , vol. abs/1911.03090, 2019
Original
2019
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning , 2019
2019
Earlier work this paper cites.
O. Kovaleva, A. Romanov, A. Rogers, and A. Rumshisky, “Revealing the dark secrets of bert,” ArXiv , vol. abs/1908.08593, 2019
Original
2019
Earlier work this paper cites.
A. Gaier and D. R. Ha, “Weight agnostic neural networks,” in Neural Information Processing Systems , 2019
2019
Earlier work this paper cites.
C. Raffel, N. M. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” ArXiv , vol. abs/1910.10683, 2019
Original
2019
Earlier work this paper cites.
C. Clark, K. Lee, M.-W. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova, “Boolq: Exploring the surprising difficulty of natural yes/no questions,” ArXiv , vol. abs/1905.10044, 2019
Original
2019
Earlier work this paper cites.
S. Oymak, Z. Fabian, M. Li, and M. Soltanolkotabi, “Generalization guarantees for neural networks via harnessing the low-rank structure of the jacobian,” ArXiv , vol. abs/1906.05392, 2019
Original
2019
Earlier work this paper cites.
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 4937–4946, 2019
2019
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020
Original
2020
Earlier work this paper cites.
J. Pfeiffer, A. Kamath, A. Rücklé, K. Cho, and I. Gurevych, “Adapterfusion: Non-destructive task composition for transfer learning,” ArXiv , vol. abs/2005.00247, 2020
Original
2020
Earlier work this paper cites.
M. Zhao, T. Lin, M. Jaggi, and H. Schütze, “Masking as an efficient alternative to finetuning for pretrained language models,” in Conference on Empirical Methods in Natural Language Processing , 2020
2020
Earlier work this paper cites.
D. Guo, A. M. Rush, and Y. Kim, “Parameter-efficient transfer learning with diff pruning,” in Annual Meeting of the Association for Computational Linguistics , 2020
2020
Earlier work this paper cites.
J. Pfeiffer, I. Vulic, I. Gurevych, and S. Ruder, “Mad-x: An adapter-based framework for multi-task cross-lingual transfer,” in Conference on Empirical Methods in Natural Language Processing , 2020
2020
Earlier work this paper cites.
A. Rücklé, G. Geigle, M. Glockner, T. Beck, J. Pfeiffer, N. Reimers, and I. Gurevych, “Adapterdrop: On the efficiency of adapters in transformers,” in Conference on Empirical Methods in Natural Language Processing , 2020
2020
Earlier work this paper cites.
T. Schick and H. Schütze, “Exploiting cloze-questions for few-shot text classification and natural language inference,” in Conference of the European Chapter of the Association for Computational Linguistics , 2020
2020
Earlier work this paper cites.
T. Shin, Y. Razeghi, R. L. L. IV, E. Wallace, and S. Singh, “Eliciting knowledge from language models using automatically generated prompts,” ArXiv , vol. abs/2010.15980, 2020
Original
2020
Earlier work this paper cites.
D. R. Radev, R. Zhang, A. Rau, A. Sivaprasad, C.-H. Hsieh, N. Rajani, X. Tang, A. Vyas, N. Verma, P. Krishna, Y. Liu, N. Irwanto, J. Pan, F. Rahman, A. Z. Zaidi, M. Mutuma, Y. Tarabar, A. Gupta, T. Yu, Y. C. Tan, X. V. Lin, C. Xiong, and R. Socher, “Dart: Open-domain structured data record to text generation,” ArXiv , vol. abs/2007.02871, 2020
Original
2020
Earlier work this paper cites.
A. Aghajanyan, L. Zettlemoyer, and S. Gupta, “Intrinsic dimensionality explains the effectiveness of language model fine-tuning,” in Annual Meeting of the Association for Computational Linguistics , 2020
2020
Earlier work this paper cites.
L. Floridi and M. Chiriatti, “Gpt-3: Its nature, scope, limits, and consequences,” Minds and Machines , 2020
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
2020
Earlier work this paper cites.
J. O. Zhang, A. Sax, A. Zamir, L. Guibas, and J. Malik, “Side-tuning: a baseline for network adaptation via additive side networks,” in European Conference on Computer Vision , 2020
2020
Earlier work this paper cites.
M. Ding, Z. Yang, W. Hong, W. Zheng, C. Zhou, D. Yin, J. Lin, X. Zou, Z. Shao, H. Yang et al. , “Cogview: Mastering text-to-image generation via transformers,” Advances in neural information processing systems , vol. 34, pp. 19 822–19 835, 2021
2021
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning . PMLR, 2021
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” ArXiv , vol. abs/2106.09685, 2021
Original
2021
Earlier work this paper cites.
X. Liu, K. Ji, Y. Fu, Z. Du, Z. Yang, and J. Tang, “P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks,” ArXiv , vol. abs/2110.07602, 2021
Original
2021
Earlier work this paper cites.
J. Gurrola-Ramos, O. Dalmau, and T. E. Alarcón, “A residual dense u-net neural network for image denoising,” IEEE Access , vol. 9, pp. 31 742–31 754, 2021
2021
Earlier work this paper cites.
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 . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2021
2021
Earlier work this paper cites.
Y. Liu, S. Agarwal, and S. Venkataraman, “Autofreeze: Automatically freezing model blocks to accelerate fine-tuning,” ArXiv , vol. abs/2102.01386, 2021
Original
2021
Earlier work this paper cites.
R. Xu, F. Luo, Z. Zhang, C. Tan, B. Chang, S. Huang, and F. Huang, “Raise a child in large language model: Towards effective and generalizable fine-tuning,” ArXiv , vol. abs/2109.05687, 2021
Original
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , vol. abs/2101.00190, 2021
Original
2021
Earlier work this paper cites.
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang, “Gpt understands, too,” ArXiv , vol. abs/2103.10385, 2021
Original
2021
Earlier work this paper cites.
Y. Gu, X. Han, Z. Liu, and M. Huang, “Ppt: Pre-trained prompt tuning for few-shot learning,” in Annual Meeting of the Association for Computational Linguistics , 2021
2021
Earlier work this paper cites.
Y. Mao, L. Mathias, R. Hou, A. Almahairi, H. Ma, J. Han, W. tau Yih, and M. Khabsa, “Unipelt: A unified framework for parameter-efficient language model tuning,” in Annual Meeting of the Association for Computational Linguistics , 2021
2021
Earlier work this paper cites.
Y.-L. Sung, V. Nair, and C. Raffel, “Training neural networks with fixed sparse masks,” in Neural Information Processing Systems , 2021
2021
Earlier work this paper cites.
E. Ben-Zaken, S. Ravfogel, and Y. Goldberg, “Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,” ArXiv , vol. abs/2106.10199, 2021
Original
2021
Earlier work this paper cites.
A. Ansell, E. Ponti, A. Korhonen, and I. Vulic, “Composable sparse fine-tuning for cross-lingual transfer,” in Annual Meeting of the Association for Computational Linguistics , 2021
2021
Earlier work this paper cites.
R. Zhang, R. Fang, W. Zhang, P. Gao, K. Li, J. Dai, Y. Qiao, and H. Li, “Tip-adapter: Training-free clip-adapter for better vision-language modeling,” arXiv preprint arXiv:2111.03930 , 2021
Original
2021
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” ArXiv , vol. abs/2104.08691, 2021
Original
2021
Earlier work this paper cites.
R. L. L. IV, I. Balavzevi’c, E. Wallace, F. Petroni, S. Singh, and S. Riedel, “Cutting down on prompts and parameters: Simple few-shot learning with language models,” in Findings , 2021
2021
Earlier work this paper cites.
T. Vu, B. Lester, N. Constant, R. Al-Rfou, and D. M. Cer, “Spot: Better frozen model adaptation through soft prompt transfer,” in Annual Meeting of the Association for Computational Linguistics , 2021
2021
Earlier work this paper cites.
P. Liu, Z.-F. Gao, W. X. Zhao, Z. Y. Xie, Z.-Y. Lu, and J. rong Wen, “Enabling lightweight fine-tuning for pre-trained language model compression based on matrix product operators,” in Annual Meeting of the Association for Computational Linguistics , 2021
2021
Earlier work this paper cites.
J. Davison, “Compacter: Efficient low-rank hypercomplex adapter layers,” in Neural Information Processing Systems , 2021
2021
Earlier work this paper cites.
J. He, C. Zhou, X. Ma, T. Berg-Kirkpatrick, and G. Neubig, “Towards a unified view of parameter-efficient transfer learning,” ArXiv , vol. abs/2110.04366, 2021
Original
2021
Earlier work this paper cites.
Y.-L. Sung, J. Cho, and M. Bansal, “Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5217–5227, 2021
2021
Earlier work this paper cites.
N. Zhang, L. Li, X. Chen, S. Deng, Z. Bi, C. Tan, F. Huang, and H. Chen, “Differentiable prompt makes pre-trained language models better few-shot learners,” ArXiv , vol. abs/2108.13161, 2021
Original
2021
Earlier work this paper cites.
Y. Su, X. Wang, Y. Qin, C.-M. Chan, Y. Lin, H. Wang, K. Wen, Z. Liu, P. Li, J. Li, L. Hou, M. Sun, and J. Zhou, “On transferability of prompt tuning for natural language processing,” in North American Chapter of the Association for Computational Linguistics , 2021
2021
Earlier work this paper cites.
P. He, B. Peng, L. Lu, S. Wang, J. Mei, Y. Liu, R. Xu, H. H. Awadalla, Y. Shi, C. Zhu et al. , “Z-code++: A pre-trained language model optimized for abstractive summarization,” arXiv preprint arXiv:2208.09770 , 2022
Original
2022
Earlier work this paper cites.
K. Zhang, K. Zhang, M. Zhang, H. Zhao, Q. Liu, W. Wu, and E. Chen, “Incorporating dynamic semantics into pre-trained language model for aspect-based sentiment analysis,” arXiv preprint arXiv:2203.16369 , 2022
Original
2022
Earlier work this paper cites.
W. Hong, M. Ding, W. Zheng, X. Liu, and J. Tang, “Cogvideo: Large-scale pretraining for text-to-video generation via transformers,” arXiv preprint arXiv:2205.15868 , 2022
Original
2022
Earlier work this paper cites.
A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia et al. , “Glm-130b: An open bilingual pre-trained model,” arXiv preprint arXiv:2210.02414 , 2022
Original
2022
Earlier work this paper cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. J. Belongie, B. Hariharan, and S. N. Lim, “Visual prompt tuning,” ArXiv , vol. abs/2203.12119, 2022
Original
2022
Earlier work this paper cites.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International conference on machine learning . PMLR, 2022
2022
Earlier work this paper cites.
Y.-L. Sung, J. Cho, and M. Bansal, “Lst: Ladder side-tuning for parameter and memory efficient transfer learning,” ArXiv , vol. abs/2206.06522, 2022
Original
2022
Earlier work this paper cites.
S. Jie and Z.-H. Deng, “Convolutional bypasses are better vision transformer adapters,” arXiv preprint arXiv:2207.07039 , 2022
Original
2022
Earlier work this paper cites.
Y. Zhang, K. Zhou, and Z. Liu, “Neural prompt search,” arXiv preprint arXiv:2206.04673 , 2022
Original
2022
Earlier work this paper cites.
X. Yang, J. Y. Huang, W. Zhou, and M. Chen, “Parameter-efficient tuning with special token adaptation,” ArXiv , vol. abs/2210.04382, 2022
Original
2022
Earlier work this paper cites.
Y. Wang, S. Mukherjee, X. Liu, J. Gao, A. H. Awadallah, and J. Gao, “Adamix: Mixture-of-adapter for parameter-efficient tuning of large language models,” ArXiv , vol. abs/2205.12410, 2022
Original
2022
Earlier work this paper cites.
C.-L. Fu, Z.-C. Chen, Y.-R. Lee, and H. yi Lee, “Adapterbias: Parameter-efficient token-dependent representation shift for adapters in nlp tasks,” ArXiv , vol. abs/2205.00305, 2022
Original
2022
Earlier work this paper cites.
S. Chen, C. Ge, Z. Tong, J. Wang, Y. Song, J. Wang, and P. Luo, “Adaptformer: Adapting vision transformers for scalable visual recognition,” Advances in Neural Information Processing Systems , 2022
2022
Earlier work this paper cites.
H. Bahng, A. Jahanian, S. Sankaranarayanan, and P. Isola, “Exploring visual prompts for adapting large-scale models,” arXiv preprint arXiv:2203.17274 , 2022
Original
2022
Earlier work this paper cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in European Conference on Computer Vision , 2022
2022
Earlier work this paper cites.
J. Wu, X. Li, C. Wei, H. Wang, A. Yuille, Y. Zhou, and C. Xie, “Unleashing the power of visual prompting at the pixel level,” arXiv preprint arXiv:2212.10556 , 2022
Original
2022
Earlier work this paper cites.
R. Gal, Y. Alaluf, Y. Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-Or, “An image is worth one word: Personalizing text-to-image generation using textual inversion,” arXiv preprint arXiv:2208.01618 , 2022
Original
2022
Earlier work this paper cites.
K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Learning to prompt for vision-language models,” International Journal of Computer Vision , 2022
2022
Earlier work this paper cites.
A. Edalati, M. S. Tahaei, I. Kobyzev, V. Nia, J. J. Clark, and M. Rezagholizadeh, “Krona: Parameter efficient tuning with kronecker adapter,” ArXiv , vol. abs/2212.10650, 2022
Original
2022
Earlier work this paper cites.
M. Parovic, G. Glavas, I. Vulic, and A. Korhonen, “Bad-x: Bilingual adapters improve zero-shot cross-lingual transfer,” in North American Chapter of the Association for Computational Linguistics , 2022
2022
Earlier work this paper cites.
S. He, L. Ding, D. Dong, M. Zhang, and D. Tao, “Sparseadapter: An easy approach for improving the parameter-efficiency of adapters,” ArXiv , vol. abs/2210.04284, 2022
Original
2022
Earlier work this paper cites.
H. Yuan, Z. Yuan, R. Gan, J. Zhang, Y. Xie, and S. Yu, “Biobart: Pretraining and evaluation of a biomedical generative language model,” in Workshop on Biomedical Natural Language Processing , 2022
2022
Earlier work this paper cites.
Y. Liu, C. An, and X. Qiu, “Y-tuning: An efficient tuning paradigm for large-scale pre-trained models via label representation learning,” ArXiv , vol. abs/2202.09817, 2022
Original
2022
Earlier work this paper cites.
B. X. Yu, J. Chang, L. Liu, Q. Tian, and C. W. Chen, “Towards a unified view on visual parameter-efficient transfer learning,” arXiv preprint arXiv:2210.00788 , 2022
Original
2022
Earlier work this paper cites.

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Later among the works it cites.
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Original
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Later among the works it cites.
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2022

Cited alongside, same era.

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Cited alongside, same era.

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2022

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Later among the works it cites.

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Later among the works it cites.

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Original

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Later among the works it cites.

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Later among the works it cites.

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Later among the works it cites.

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2024

Later among the works it cites.

D. Zhang, Z. Hu, S. Zhoubian, Z. Du, K. Yang, Z. Wang, Y. Yue, Y. Dong, and J. Tang, “Sciglm: Training scientific language models with self-reflective instruction annotation and tuning,” arXiv preprint arXiv:2401.07950 , 2024

2024

Later among the works it cites.

D. Zhang, S. Zhoubian, Y. Yue, Y. Dong, and J. Tang, “Rest-mcts*: Llm self-training via process reward guided tree search,” arXiv preprint arXiv:2406.03816 , 2024

2024

Later among the works it cites.

L. Xue, D. Zhang, Y. Dong, and J. Tang, “Autore: Document-level relation extraction with large language models,” arXiv preprint arXiv:2403.14888 , 2024

2024

Later among the works it cites.

X. Xia, D. Zhang, Z. Liao, Z. Hou, T. Sun, J. Li, L. Fu, and Y. Dong, “Scenegenagent: Precise industrial scene generation with coding agent,” arXiv preprint arXiv:2410.21909 , 2024

2024

Later among the works it cites.

J. Xu, X. Liu, Y. Wu, Y. Tong, Q. Li, M. Ding, J. Tang, and Y. Dong, “Imagereward: Learning and evaluating human preferences for text-to-image generation,” Advances in Neural Information Processing Systems , vol. 36, 2024

2024

Later among the works it cites.

Z. Yang, J. Teng, W. Zheng, M. Ding, S. Huang, J. Xu, Y. Yang, W. Hong, X. Zhang, G. Feng et al. , “Cogvideox: Text-to-video diffusion models with an expert transformer,” arXiv preprint arXiv:2408.06072 , 2024

2024

Later among the works it cites.

T. GLM, A. Zeng, B. Xu, B. Wang, C. Zhang, D. Yin, D. Rojas, G. Feng, H. Zhao, H. Lai et al. , “Chatglm: A family of large language models from glm-130b to glm-4 all tools,” arXiv preprint arXiv:2406.12793 , 2024

2024

Later among the works it cites.

Y. Liu, K. Zhang, Y. Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y. Huang, H. Sun, J. Gao et al. , “Sora: A review on background, technology, limitations, and opportunities of large vision models,” arXiv preprint arXiv:2402.17177 , 2024

2024

Later among the works it cites.

H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 26 296–26 306

2024

Later among the works it cites.

Y. Xin, S. Luo, H. Zhou, J. Du, X. Liu, Y. Fan, Q. Li, and Y. Du, “Parameter-efficient fine-tuning for pre-trained vision models: A survey,” arXiv preprint arXiv:2402.02242 , 2024

2024

Later among the works it cites.

Z. Han, C. Gao, J. Liu, J. Zhang, and S. Q. Zhang, “Parameter-efficient fine-tuning for large models: A comprehensive survey,” arXiv preprint arXiv:2403.14608 , 2024

2024

Later among the works it cites.

X. Zhou, J. He, Y. Ke, G. Zhu, V. Gutiérrez-Basulto, and J. Z. Pan, “An empirical study on parameter-efficient fine-tuning for multimodal large language models,” arXiv preprint arXiv:2406.05130 , 2024

2024

Later among the works it cites.

L. Wang, S. Chen, L. Jiang, S. Pan, R. Cai, S. Yang, and F. Yang, “Parameter-efficient fine-tuning in large models: A survey of methodologies,” arXiv preprint arXiv:2410.19878 , 2024

2024

Later among the works it cites.

W. Hong, W. Wang, M. Ding, W. Yu, Q. Lv, Y. Wang, Y. Cheng, S. Huang, J. Ji, Z. Xue et al. , “Cogvlm2: Visual language models for image and video understanding,” arXiv preprint arXiv:2408.16500 , 2024

2024

Later among the works it cites.

M. Reid, N. Savinov, D. Teplyashin, D. Lepikhin, T. Lillicrap, J.-b. Alayrac, R. Soricut, A. Lazaridou, O. Firat, J. Schrittwieser et al. , “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,” arXiv preprint arXiv:2403.05530 , 2024

2024

Later among the works it cites.

Z. Tang, Z. Yang, C. Zhu, M. Zeng, and M. Bansal, “Any-to-any generation via composable diffusion,” Advances in Neural Information Processing Systems , 2024

2024

Later among the works it cites.

Z. Tang, Z. Yang, M. Khademi, Y. Liu, C. Zhu, and M. Bansal, “Codi-2: In-context interleaved and interactive any-to-any generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

Y. Ge, S. Zhao, J. Zhu, Y. Ge, K. Yi, L. Song, C. Li, X. Ding, and Y. Shan, “Seed-x: Multimodal models with unified multi-granularity comprehension and generation,” arXiv preprint arXiv:2404.14396 , 2024

2024

Later among the works it cites.

Y. Gu, X. Wang, J. Z. Wu, Y. Shi, Y. Chen, Z. Fan, W. Xiao, R. Zhao, S. Chang, W. Wu et al. , “Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models,” Advances in Neural Information Processing Systems , 2024

2024

Later among the works it cites.

Q. Yu, J. He, X. Deng, X. Shen, and L.-C. Chen, “Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip,” Advances in Neural Information Processing Systems , 2024

2024

Later among the works it cites.

M. Fu, K. Zhu, and J. Wu, “Dtl: Disentangled transfer learning for visual recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

C. Mou, X. Wang, L. Xie, Y. Wu, J. Zhang, Z. Qi, and Y. Shan, “T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

X. Guo, M. Zheng, L. Hou, Y. Gao, Y. Deng, P. Wan, D. Zhang, Y. Liu, W. Hu, Z. Zha et al. , “I2v-adapter: A general image-to-video adapter for diffusion models,” in ACM SIGGRAPH 2024 Conference Papers , 2024

2024

Later among the works it cites.

B. Peng, J. Wang, Y. Zhang, W. Li, M.-C. Yang, and J. Jia, “Controlnext: Powerful and efficient control for image and video generation,” arXiv preprint arXiv:2408.06070 , 2024

2024

Later among the works it cites.

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,” International Journal of Computer Vision , 2024

2024

Later among the works it cites.

H. Wang, J. Chang, Y. Zhai, X. Luo, J. Sun, Z. Lin, and Q. Tian, “Lion: Implicit vision prompt tuning,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

S. Marjit, H. Singh, N. Mathur, S. Paul, C.-M. Yu, and P.-Y. Chen, “Diffusekrona: A parameter efficient fine-tuning method for personalized diffusion model,” arXiv preprint arXiv:2402.17412 , 2024

2024

Later among the works it cites.

Y. Ren, Y. Zhou, J. Yang, J. Shi, D. Liu, F. Liu, M. Kwon, and A. Shrivastava, “Customize-a-video: One-shot motion customization of text-to-video diffusion models,” ECCV , 2024

2024

Later among the works it cites.

L. Song, Y. Chen, S. Yang, X. Ding, Y. Ge, Y.-C. Chen, and Y. Shan, “Low-rank approximation for sparse attention in multi-modal llms,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

T. Wu, J. Wang, Z. Zhao, and N. Wong, “Mixture-of-subspaces in low-rank adaptation,” arXiv preprint arXiv:2406.11909 , 2024

2024

Later among the works it cites.

H. Wang, T. Sun, K. Ji, J. Wang, C. Fan, and J. Gu, “Orchmoe: Efficient multi-adapter learning with task-skill synergy,” arXiv preprint arXiv:2401.10559 , 2024

2024

Later among the works it cites.

C. Liu, K. Sun, Q. Zhou, Y. Duan, J. Shu, H. Kan, Z. Gu, and J. Hu, “Cpmi-chatglm: Parameter-efficient fine-tuning chatglm with chinese patent medicine instructions,” Scientific Reports , vol. 14, no. 1, p. 6403, 2024

2024

Later among the works it cites.

Y. Zheng, R. Zhang, J. Zhang, Y. Ye, and Z. Luo, “Llamafactory: Unified efficient fine-tuning of 100+ language models,” arXiv preprint arXiv:2403.13372 , 2024

2024

Later among the works it cites.

S.-Y. Liu, C.-Y. Wang, H. Yin, P. Molchanov, Y.-C. F. Wang, K.-T. Cheng, and M.-H. Chen, “Dora: Weight-decomposed low-rank adaptation,” arXiv preprint arXiv:2402.09353 , 2024

2024

Later among the works it cites.

F. Meng, Z. Wang, and M. Zhang, “Pissa: Principal singular values and singular vectors adaptation of large language models,” arXiv preprint arXiv:2404.02948 , 2024

2024

Later among the works it cites.

H. H. Zhao, P. Wang, Y. Zhao, H. Luo, F. Wang, and M. Z. Shou, “Sct: A simple baseline for parameter-efficient fine-tuning via salient channels,” International Journal of Computer Vision , 2024

2024

Later among the works it cites.

Y. Xin, J. Du, Q. Wang, Z. Lin, and K. Yan, “Vmt-adapter: Parameter-efficient transfer learning for multi-task dense scene understanding,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

S. Basu, S. Hu, D. Massiceti, and S. Feizi, “Strong baselines for parameter-efficient few-shot fine-tuning,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

D. Yin, L. Hu, B. Li, Y. Zhang, and X. Yang, “5%¿ 100%: Breaking performance shackles of full fine-tuning on visual recognition tasks,” arXiv preprint arXiv:2408.08345 , 2024

2024

Later among the works it cites.

W. G. C. Bandara and V. M. Patel, “Attention prompt tuning: Parameter-efficient adaptation of pre-trained models for spatiotemporal modeling,” arXiv preprint arXiv:2403.06978 , 2024

2024

Later among the works it cites.

A. Gupta, G. Mittal, A. Magooda, Y. Yu, G. W. Taylor, and M. Chen, “Losa: Long-short-range adapter for scaling end-to-end temporal action localization,” arXiv preprint arXiv:2404.01282 , 2024

2024

Later among the works it cites.

S. Zhang, J. Zhang, H. Zhang, and L. Zhuo, “Rastformer: region-aware spatiotemporal transformer for visual homogenization recognition in short videos,” Neural Computing and Applications , 2024

2024

Later among the works it cites.

Y. Yao, A. Zhang, Z. Zhang, Z. Liu, T.-S. Chua, and M. Sun, “Cpt: Colorful prompt tuning for pre-trained vision-language models,” AI Open , 2024

2024

Later among the works it cites.

J. Bai, K. Gao, S. Min, S.-T. Xia, Z. Li, and W. Liu, “Badclip: Trigger-aware prompt learning for backdoor attacks on clip,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

X. Wang, Y. Yang, M. Zhu, K. Zheng, S. Liu, and W. Chen, “Mept: Multi-representation guided prompt tuning for vision-language model,” arXiv preprint arXiv:2408.09706 , 2024

2024

Later among the works it cites.

Y. Zhang, C.-W. Cheng, K. Yu, Z. He, C.-B. Schönlieb, and A. I. Aviles-Rivero, “Node-adapter: Neural ordinary differential equations for better vision-language reasoning,” arXiv preprint arXiv:2407.08672 , 2024

2024

Later among the works it cites.

G. Kim, S. Kim, and S. Lee, “Aapl: Adding attributes to prompt learning for vision-language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

K. Goswami, S. Karanam, P. Udhayanan, K. Joseph, and B. V. Srinivasan, “Copl: Contextual prompt learning for vision-language understanding,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

Z. Xiao, J. Shen, M. M. Derakhshani, S. Liao, and C. G. Snoek, “Any-shift prompting for generalization over distributions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

M. Dorkenwald, N. Barazani, C. G. Snoek, and Y. M. Asano, “Pin: Positional insert unlocks object localisation abilities in vlms,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

J. Silva-Rodriguez, S. Hajimiri, I. Ben Ayed, and J. Dolz, “A closer look at the few-shot adaptation of large vision-language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

H. Yao, R. Zhang, and C. Xu, “Tcp: Textual-based class-aware prompt tuning for visual-language model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

J. Zhang, S. Wu, L. Gao, H. T. Shen, and J. Song, “Dept: Decoupled prompt tuning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

H. Shi, S. D. Dao, and J. Cai, “Llmformer: Large language model for open-vocabulary semantic segmentation,” International Journal of Computer Vision , 2024

2024

Later among the works it cites.

M. Wang, J. Xing, B. Jiang, J. Chen, J. Mei, X. Zuo, G. Dai, J. Wang, and Y. Liu, “A multimodal, multi-task adapting framework for video action recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

B. Wang and W. Wang, “Tds-clip: Temporal difference side network for image-to-video transfer learning,” arXiv preprint arXiv:2408.10688 , 2024

Original

2024

Later among the works it cites.

M. Liu, B. Li, and Y. Yu, “Omniclip: Adapting clip for video recognition with spatial-temporal omni-scale feature learning,” arXiv preprint arXiv:2408.06158 , 2024

2024

Later among the works it cites.

Y. Wang, X. Jiang, D. Cheng, D. Li, and C. Zhao, “Actprompt: In-domain feature adaptation via action cues for video temporal grounding,” arXiv preprint arXiv:2408.06622 , 2024

2024

Later among the works it cites.

X. Jin, B. Zhang, W. Gong, K. Xu, X. Deng, P. Wang, Z. Zhang, X. Shen, and J. Feng, “Mv-adapter: Multimodal video transfer learning for video text retrieval,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

X. Huang, Z. Huang, S. Li, W. Qu, T. He, Y. Hou, Y. Zuo, and W. Ouyang, “Frozen clip transformer is an efficient point cloud encoder,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024

2024

Later among the works it cites.

X. Zhou, D. Liang, W. Xu, X. Zhu, Y. Xu, Z. Zou, and X. Bai, “Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

Y. Sun, J. Chen, S. Zhang, X. Zhang, Q. Chen, G. Zhang, E. Ding, J. Wang, and Z. Li, “Vrp-sam: Sam with visual reference prompt,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

S. Zhao, D. Chen, Y.-C. Chen, J. Bao, S. Hao, L. Yuan, and K.-Y. K. Wong, “Uni-controlnet: All-in-one control to text-to-image diffusion models,” Advances in Neural Information Processing Systems , 2024

2024

Later among the works it cites.

J. Xiao, K. Zhu, H. Zhang, Z. Liu, Y. Shen, Z. Yang, R. Feng, Y. Liu, X. Fu, and Z.-J. Zha, “Ccm: Real-time controllable visual content creation using text-to-image consistency models,” in arXiv preprint arXiv:2312.06971 , 2024

2024

Later among the works it cites.

H. Lin, J. Cho, A. Zala, and M. Bansal, “Ctrl-adapter: An efficient and versatile framework for adapting diverse controls to any diffusion model,” arXiv preprint arXiv:2404.09967 , 2024

2024

Later among the works it cites.

L. Ran, X. Cun, J.-W. Liu, R. Zhao, S. Zijie, X. Wang, J. Keppo, and M. Z. Shou, “X-adapter: Adding universal compatibility of plugins for upgraded diffusion model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

Y. Yang, W. Wang, L. Peng, C. Song, Y. Chen, H. Li, X. Yang, Q. Lu, D. Cai, B. Wu et al. , “Lora-composer: Leveraging low-rank adaptation for multi-concept customization in training-free diffusion models,” arXiv preprint arXiv:2403.11627 , 2024

2024

Later among the works it cites.

D. A. Hudson, D. Zoran, M. Malinowski, A. K. Lampinen, A. Jaegle, J. L. McClelland, L. Matthey, F. Hill, and A. Lerchner, “Soda: Bottleneck diffusion models for representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

Z. Xing, Q. Dai, H. Hu, Z. Wu, and Y.-G. Jiang, “Simda: Simple diffusion adapter for efficient video generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

H. Chefer, S. Zada, R. Paiss, A. Ephrat, O. Tov, M. Rubinstein, L. Wolf, T. Dekel, T. Michaeli, and I. Mosseri, “Still-moving: Customized video generation without customized video data,” arXiv preprint arXiv:2407.08674 , 2024

2024

Later among the works it cites.

R. Feng, W. Weng, Y. Wang, Y. Yuan, J. Bao, C. Luo, Z. Chen, and B. Guo, “Ccedit: Creative and controllable video editing via diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

K. Zhang, Y. Zhou, X. Xu, B. Dai, and X. Pan, “Diffmorpher: Unleashing the capability of diffusion models for image morphing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

J. Cheng, P. Xie, X. Xia, J. Li, J. Wu, Y. Ren, H. Li, X. Xiao, M. Zheng, and L. Fu, “Resadapter: Domain consistent resolution adapter for diffusion models,” arXiv preprint arXiv:2403.02084 , 2024

2024

Later among the works it cites.

J. Guo, X. Xu, Y. Pu, Z. Ni, C. Wang, M. Vasu, S. Song, G. Huang, and H. Shi, “Smooth diffusion: Crafting smooth latent spaces in diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

M. Cai, H. Liu, S. K. Mustikovela, G. P. Meyer, Y. Chai, D. Park, and Y. J. Lee, “Vip-llava: Making large multimodal models understand arbitrary visual prompts,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

Z. Li, B. Yang, Q. Liu, Z. Ma, S. Zhang, J. Yang, Y. Sun, Y. Liu, and X. Bai, “Monkey: Image resolution and text label are important things for large multi-modal models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. Zhuang, “Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,” Advances in Neural Information Processing Systems , 2024

2024

Later among the works it cites.

J. Xie, W. Mao, Z. Bai, D. J. Zhang, W. Wang, K. Q. Lin, Y. Gu, Z. Chen, Z. Yang, and M. Z. Shou, “Show-o: One single transformer to unify multimodal understanding and generation,” arXiv preprint arXiv:2408.12528 , 2024

2024

Later among the works it cites.

E. Chern, J. Su, Y. Ma, and P. Liu, “Anole: An open, autoregressive, native large multimodal models for interleaved image-text generation,” arXiv preprint arXiv:2407.06135 , 2024

2024

Later among the works it cites.