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Parameter-Efficient Fine-Tuning (PEFT) is a technique that allows us to adapt powerful Foundation Models (FMs) to diverse downstream tasks while preserving and unleashing their inherent capabilities.
Use of the stair vision library within the ISPRS 2D semantic labeling benchmark (Vaihingen)
Markus Gerke, I. 2014 · 2014
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Fully convolutional networks for semantic segmentation
Long, J.; Shelhamer, E.; and Darrell, T. 2015 · 2015
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Fully convolutional networks for semantic segmentation
Shelhamer, E.; Long, J.; and Darrell, T. 2016 · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
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Adversarial reprogramming of neural networks
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Decoupled Weight Decay Regularization
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iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
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MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark
Contributors, M. 2020 · 2020
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A survey on algorithm research of scene parsing based on deep learning
Rui, Z.; and Jintao, L. 2020 · 2020
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Land-cover classification with high-resolution remote sensing images using transferable deep models
Tong, X.-Y.; Xia, G.-S.; Lu, Q.; Shen, H.; Li, S.; You, S.; and Zhang, L. 2020 · 2020
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An empirical study of training self-supervised vision transformers
Chen, X.; Xie, S.; and He, K. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C.; Yang, Y.; Xia, Y.; Chen, Y.-T.; Parekh, Z.; Pham, H.; Le, Q.; Sung, Y.-H.; Li, Z.; and Duerig, T. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Dacs: Domain adaptation via cross-domain mixed sampling
Tranheden, W.; Olsson, V.; Pinto, J.; and Svensson, L. 2021 · 2021
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LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation
Wang, J.; Zheng, Z.; Ma, A.; Lu, X.; and Zhong, Y. 2021 · 2021
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Open-vocabulary object detection using captions
Zareian, A.; Rosa, K. D.; Hu, D. H.; and Chang, S.-F. 2021 · 2021
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Masked-attention mask transformer for universal image segmentation
Cheng, B.; Misra, I.; Schwing, A. G.; Kirillov, A.; and Girdhar, R. 2022 · 2022
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Visual prompt tuning for test-time domain adaptation
Gao, Y.; Shi, X.; Zhu, Y.; Wang, H.; Tang, Z.; Zhou, X.; Li, M.; and Metaxas, D. N. 2022 · 2022
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Visual prompt tuning
Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022 · 2022
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Sigmoid loss for language image pre-training
Zhai, X.; Mustafa, B.; Kolesnikov, A.; and Beyer, L. 2023 · 2023
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Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory
Zhu, J.; Guo, Y.; Sun, G.; Yang, L.; Deng, M.; and Chen, J. 2023 · 2023
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MTLoRA: Low-Rank Adaptation Approach for Efficient Multi-Task Learning
Agiza, A.; Neseem, M.; and Reda, S. 2024 · 2024
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Learning frequency-adapted vision foundation model for domain generalized semantic segmentation
Bi, Q.; Yi, J.; Zheng, H.; Zhan, H.; Huang, Y.; Ji, W.; Li, Y.; and Zheng, Y. 2024 · 2024
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Vision Transformers Need Registers
Darcet, T.; Oquab, M.; Mairal, J.; and Bojanowski, P. 2024 · 2024
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Simple open-vocabulary object detection
Minderer, M.; Gritsenko, A.; Stone, A.; Neumann, M.; Weissenborn, D.; Dosovitskiy, A.; Mahendran, A.; Arnab, A.; Dehghani, M.; Shen, Z.; et al. 2022 · 2022
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Learning to prompt for vision-language models
Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2022 · 2022
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Dai, W.; Li, J.; Li, D.; Tiong, A.; Zhao, J.; Wang, W.; Li, B.; Fung, P. N.; and Hoi, S. 2023 · 2023
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Domain adaptation via prompt learning
Ge, C.; Huang, R.; Xie, M.; Lai, Z.; Song, S.; Li, S.; and Huang, G. 2023 · 2023
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Frequency-Based Optimal Style Mix for Domain Generalization in Semantic Segmentation of Remote Sensing Images
Iizuka, R.; Xia, J.; and Yokoya, N. 2023 · 2023
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Visual instruction tuning
Liu, H.; Li, C.; Wu, Q.; and Lee, Y. J. 2023 · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M.; Darcet, T.; Moutakanni, T.; Vo, H.; Szafraniec, M.; Khalidov, V.; Fernandez, P.; Haziza, D.; Massa, F.; El-Nouby, A.; et al. 2023 · 2023
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Dong, Z.; Gu, Y.; and Liu, T. 2024 · 2024
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CoDA: Instructive chain-of-domain adaptation with severity-aware visual prompt tuning
Gong, Z.; Li, F.; Deng, Y.; Bhattacharjee, D.; Ma, X.; Zhu, X.; and Ji, Z. 2024 · 2024
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TEA: A training-efficient adapting framework for tuning foundation models in remote sensing
Hu, L.; Lu, W.; Yu, H.; Yin, D.; Sun, X.; and Fu, K. 2024 · 2024
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Single Domain Generalization Method for Remote Sensing Image Segmentation via Category Consistency on Domain Randomization
Liang, C.; Li, W.; Dong, Y.; and Fu, W. 2024 · 2024
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Luo, G.; Yang, X.; Dou, W.; Wang, Z.; Dai, J.; Qiao, Y.; and Zhu, X. 2024 · 2024
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MTP: Advancing remote sensing foundation model via multi-task pretraining
Wang, D.; Zhang, J.; Xu, M.; Liu, L.; Wang, D.; Gao, E.; Han, C.; Guo, H.; Du, B.; Tao, D.; et al. 2024 · 2024
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Stronger Fewer & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Wei, Z.; Chen, L.; Jin, Y.; Ma, X.; Liu, T.; Ling, P.; Wang, B.; Chen, H.; and Zheng, J. 2024 · 2024
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Neural plasticity-inspired foundation model for observing the Earth crossing modalities
Xiong, Z.; Wang, Y.; Zhang, F.; Stewart, A. J.; Hanna, J.; Borth, D.; Papoutsis, I.; Le Saux, B.; Camps-Valls, G.; and Zhu, X. X. 2024 · 2024
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Low-Rank Adaption on Transformer-based Oriented Object Detector for Satellite Onboard Processing of Remote Sensing Images
Pu, X.; and Xu, F. 2025 · 2025
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