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Fine-tuning pre-trained models with custom data leads to numerous expert models on specific tasks.
The mnist database of handwritten digits
Yann LeCun · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al · 2011
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The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Referitgame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
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Generation and comprehension of unambiguous object descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
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Sun database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A. Plummer, Liwei Wang, Christopher M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2017
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Vizwiz grand challenge: Answering visual questions from blind people
Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham · 2018
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Ocr-vqa: Visual question answering by reading text in images
Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, and Anirban Chakraborty · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
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Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei Zhang, Zhou Yu, Xiaodan Liang, and Song-Chun Zhu · 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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Screen2words: Automatic mobile ui summarization with multimodal learning
Bryan Wang, Gang Li, Xin Zhou, Zhourong Chen, Tovi Grossman, and Yang Li · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 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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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 2022
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Learn to explain: Multimodal reasoning via thought chains for science question answering
Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan · 2022
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Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, et al · 2022
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A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi · 2022
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
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Dora: Weight-decomposed low-rank adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen · 2024
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Pissa: Principal singular values and singular vectors adaptation of large language models
Fanxu Meng, Zhaohui Wang, and Muhan Zhang · 2024
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Ziplora: Any subject in any style by effectively merging loras
Viraj Shah, Nataniel Ruiz, Forrester Cole, Erika Lu, Svetlana Lazebnik, Yuanzhen Li, and Varun Jampani · 2024
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Zipit! merging models from different tasks without training
George Stoica, Daniel Bolya, Jakob Brandt Bjorner, Pratik Ramesh, Taylor Hearn, and Judy Hoffman · 2024
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Merging multi-task models via weight-ensembling mixture of experts
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Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al · 2023
Cited alongside, same era.
Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin · 2023
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2023
Cited alongside, same era.
Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng · 2023
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Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Cited alongside, same era.
Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen · 2023
Cited alongside, same era.
Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, and Dacheng Tao · 2024
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Parameter-efficient multi-task model fusion with partial linearization
Anke Tang, Li Shen, Yong Luo, Yibing Zhan, Han Hu, Bo Du, Yixin Chen, and Dacheng Tao · 2024
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HydraloRA: An asymmetric loRA architecture for efficient fine-tuning
Chunlin Tian, Zhan Shi, Zhijiang Guo, Li Li, and Cheng zhong Xu · 2024
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2024
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Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al · 2024
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Mixture-of-subspaces in low-rank adaptation
Taiqiang Wu, Jiahao Wang, Zhe Zhao, and Ngai Wong · 2024
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Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal · 2024
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Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities
Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, and Dacheng Tao · 2024
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Adamerging: Adaptive model merging for multi-task learning
Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, and Dacheng Tao · 2024
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li · 2024
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Modalprompt: Dual-modality guided prompt for continual learning of large multimodal models
Fanhu Zeng, Fei Zhu, Haiyang Guo, Xu-Yao Zhang, and Cheng-Lin Liu · 2024
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Asymmetry in low-rank adapters of foundation models
Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, and Justin Solomon · 2024
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Hide-llava: Hierarchical decoupling for continual instruction tuning of multimodal large language model
Haiyang Guo, Fanhu Zeng, Ziwei Xiang, Fei Zhu, Da-Han Wang, Xu-Yao Zhang, and Cheng-Lin Liu · 2025
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Federated continual instruction tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu, Wenzhuo Liu, Da-Han Wang, Jian Xu, Xu-Yao Zhang, and Cheng-Lin Liu · 2025
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Rora: Efficient fine-tuning of llm with reliability optimization for rank adaptation
Jun Liu, Zhenglun Kong, Peiyan Dong, Xuan Shen, Pu Zhao, Hao Tang, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, et al · 2025
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Mmbench: Is your multi-modal model an all-around player?
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al · 2025
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Local-prompt: Extensible local prompts for few-shot out-of-distribution detection
Fanhu Zeng, Zhen Cheng, Fei Zhu, Hongxin Wei, and Xu-Yao Zhang · 2025
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Trustlora: Low-rank adaptation for failure detection under out-of-distribution data
Fei Zhu and Zhaoxiang Zhang · 2025
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