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Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks.
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Sun database: Large-scale scene recognition from abbey to zoo
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Cats and dogs
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Ucf101: A dataset of 101 human actions classes from videos in the wild
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Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
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Auto-encoding variational bayes
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3d object representations for fine-grained categorization
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Food-101 – mining discriminative components with random forests
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Describing textures in the wild
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Generative adversarial nets
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ImageNet Large Scale Visual Recognition Challenge
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar · 2018
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Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
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Exploring visual prompts for adapting large-scale models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola · 2022
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Visual prompting via image inpainting
Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei Efros · 2022
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Model reprogramming: Resource-efficient cross-domain machine learning
Pin-Yu 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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Design guidelines for prompt engineering text-to-image generative models
Vivian Liu and Lydia B Chilton · 2022
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
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Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al · 2019
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Adversarial reprogramming of neural networks
Gamaleldin F. Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein · 2019
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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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Kornia: an open source differentiable computer vision library for pytorch
E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski · 2020
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Jochem Loedeman, Maarten C Stol, Tengda Han, and Yuki M Asano · 2022
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Simple open-vocabulary object detection
Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Neural clamping: Joint input perturbation and temperature scaling for neural network calibration
Yung-Chen Tang, Pin-Yu Chen, and Tsung-Yi Ho · 2022
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Unleashing the power of visual prompting at the pixel level
Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, and Cihang Xie · 2022
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Reprogrammable-FL: Improving utility-privacy tradeoff in federated learning via model reprogramming
Huzaifa Arif, Alex Gittens, and Pin-Yu Chen · 2023
Closest in time.
Visual prompting for adversarial robustness
Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, and Sijia Liu · 2023
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NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
Lei Hsiung, Yung-Chen Tang, Pin-Yu Chen, and Tsung-Yi Ho · 2023
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Rethinking visual prompt learning as masked visual token modeling
Ning Liao, Bowen Shi, Min Cao, Xiaopeng Zhang, Qi Tian, and Junchi Yan · 2023
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Blackvip: Black-box visual prompting for robust transfer learning
Changdae Oh, Hyeji Hwang, Hee-young Lee, YongTaek Lim, Geunyoung Jung, Jiyoung Jung, Hosik Choi, and Kyungwoo Song · 2023
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Prompt space optimizing few-shot reasoning success with large language models
Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, and Xiaodong Lin · 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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Hao-Lun Sun, Lei Hsiung, Nandhini Chandramoorthy, Pin-Yu Chen, and Tsung-Yi Ho · 2023
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Convolutional visual prompt for robust visual perception
Yun-Yun Tsai, Chengzhi Mao, and Junfeng Yang · 2023
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From visual prompt learning to zero-shot transfer: Mapping is all you need
Ziqing Yang, Zeyang Sha, Michael Backes, and Yang Zhang · 2023
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