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Transformer-based large pre-trained models have shown remarkable generalization ability, and various parameter-efficient fine-tuning (PEFT) methods have been proposed to customize these models on downstream tasks with minimal computational and memory budgets.
Automated flower classification over a large number of classes
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Novel dataset for fine-grained image categorization: Stanford dogs
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The caltech-ucsd birds-200-2011 dataset
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Fine-grained car detection for visual census estimation
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
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Attention is all you need
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Dcfnet: Deep neural network with decomposed convolutional filters
Qiang Qiu, Xiuyuan Cheng, Guillermo Sapiro, et al · 2018
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Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Stochastic conditional generative networks with basis decomposition
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, and Qiang Qiu · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
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Language models are few-shot learners
Tom B Brown · 2020
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Xiuyuan Cheng, Zichen Miao, and Qiang Qiu · 2020
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Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 2020
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Graph convolution with low-rank learnable local filters
Xiuyuan Cheng, Zichen Miao, and Qiang Qiu · 2021
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Unifying vision-and-language tasks via text generation
Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal · 2021
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Attention is not all you need: Pure attention loses rank doubly exponentially with depth
Yihe Dong, Jean-Baptiste Cordonnier, and Andreas Loukas · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2021
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Lora: Low-rank adaptation of large language models
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Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
Globalmapper: Arbitrary-shaped urban layout generation
Liu He and Daniel Aliaga · 2023
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Diffusion-based document layout generation
Liu He, Yijuan Lu, John Corring, Dinei Florencio, and Cha Zhang · 2023
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Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Multi-concept customization of text-to-image diffusion
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Deepvit: Towards deeper vision transformer
Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, and Jiashi Feng · 2021
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Adaptformer: Adapting vision transformers for scalable visual recognition
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, and Ping Luo · 2022
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The vendi score: A diversity evaluation metric for machine learning
Dan Friedman and Adji Bousso Dieng · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Learning to retain while acquiring: Combating distribution-shift in adversarial data-free knowledge distillation
Gaurav Patel, Konda Reddy Mopuri, and Qiang Qiu · 2023
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Controlling text-to-image diffusion by orthogonal finetuning
Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Schölkopf · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Visual prompt tuning for generative transfer learning
Kihyuk Sohn, Huiwen Chang, José Lezama, Luisa Polania, Han Zhang, Yuan Hao, Irfan Essa, and Lu Jiang · 2023
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Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning
Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, and Zhenguo Li · 2023
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Navigating text-to-image customization: From lycoris fine-tuning to model evaluation
SHIH-YING YEH, Yu-Guan Hsieh, Zhidong Gao, Bernard BW Yang, Giyeong Oh, and Yanmin Gong · 2023
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Stabilizing transformer training by preventing attention entropy collapse
Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin, Dan Busbridge, Jason Ramapuram, Yizhe Zhang, Jiatao Gu, and Joshua M Susskind · 2023
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Coho: Context-sensitive city-scale hierarchical urban layout generation
Liu He and Daniel Aliaga · 2024
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Kubrick: Multimodal agent collaborations for synthetic video generation
Liu He, Yizhi Song, Hejun Huang, Daniel Aliaga, and Xin Zhou · 2024
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Vera: Vector-based random matrix adaptation
Dawid J Kopiczko, Tijmen Blankevoort, and Yuki M Asano · 2024
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Efficient source-free time-series adaptation via parameter subspace disentanglement
Gaurav Patel, Christopher Sandino, Behrooz Mahasseni, Ellen L Zippi, Erdrin Azemi, Ali Moin, and Juri Minxha · 2024
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2024
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Thinking outside the bbox: Unconstrained generative object compositing
Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang, Jianming Zhang, Yizhi Song, Dan Ruta, Andrew Gilbert, John Collomosse, and Soo Ye Kim · 2024
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Haoyu Wang, Tianci Liu, Ruirui Li, Monica Cheng, Tuo Zhao, and Jing Gao · 2024
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Groundingbooth: Grounding text-to-image customization
Zhexiao Xiong, Wei Xiong, Jing Shi, He Zhang, Yizhi Song, and Nathan Jacobs · 2024
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Neat: Nonlinear parameter-efficient adaptation of pre-trained models
Yibo Zhong, Haoxiang Jiang, Lincan Li, Ryumei Nakada, Tianci Liu, Linjun Zhang, Huaxiu Yao, and Haoyu Wang · 2024
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Tianci Liu, Ruirui Li, Yunzhe Qi, Hui Liu, Xianfeng Tang, Tianqi Zheng, Qingyu Yin, Monica Xiao Cheng, Jun Huan, Haoyu Wang, et al · 2025
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Learning to unlearn while retaining: Combating gradient conflicts in machine unlearning
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