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The visual prompts have provided an efficient manner in addressing visual cross-domain problems.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Lin, G.; Milan, A.; Shen, C.; and Reid, I. 2017 · 1934
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Deep ordinal regression network for monocular depth estimation
Fu, H.; Gong, M.; Wang, C.; Batmanghelich, K.; and Tao, D. 2018 · 2011
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
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Indoor Segmentation and Support Inference from RGBD Images
Nathan Silberman, P. K., Derek Hoiem; and Fergus, R. 2012 · 2012
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D.; Puhrsch, C.; and Fergus, R. 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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High-resolution stereo datasets with subpixel-accurate ground truth
Scharstein, D.; Hirschmüller, H.; Kitajima, Y.; Krathwohl, G.; Nešić, N.; Wang, X.; and Westling, P. 2014 · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y.; and Lempitsky, V. 2015 · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y.; and Ghahramani, Z. 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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Pyramid scene parsing network
Zhao, H.; Shi, J.; Qi, X.; Wang, X.; and Jia, J. 2017 · 2017
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Adadepth: Unsupervised content congruent adaptation for depth estimation
Kundu, J. N.; Uppala, P. K.; Pahuja, A.; and Babu, R. V. 2018 · 2018
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Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Xu, D.; Wang, W.; Tang, H.; Liu, H.; Sebe, N.; and Ricci, E. 2018 · 2018
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T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks
Zheng, C.; Cham, T.-J.; and Cai, J. 2018 · 2018
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Monocular depth estimation using relative depth maps
Lee, J.-H.; and Kim, C.-S. 2019 · 2019
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SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation
Ramamonjisoa, M.; and Lepetit, V. 2019 · 2019
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DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios
Yang, G.; Song, X.; Huang, C.; Deng, Z.; Shi, J.; and Zhou, B. 2019 · 2019
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Geometry-aware symmetric domain adaptation for monocular depth estimation
Zhao, S.; Fu, H.; Gong, M.; and Tao, D. 2019 · 2019
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
Cited alongside, same era.
Universal Source-Free Domain Adaptation
Kundu, J. N.; Venkat, N.; M, R.; and Babu, R. V. 2020 · 2020
Cited alongside, same era.
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Liang, J.; Hu, D.; and Feng, J. 2020 · 2020
Cited alongside, same era.
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields
Ramamonjisoa, M.; Du, Y.; and Lepetit, V. 2020 · 2020
Cited alongside, same era.
Fda: Fourier domain adaptation for semantic segmentation
Yang, Y.; and Soatto, S. 2020 · 2020
Cited alongside, same era.
Efficient Transfer Learning for Visual Tasks via Continuous Optimization of Prompts
Conder, J.; Jefferson, J.; Pages, N.; Jawed, K.; Nejati, A.; and Sagar, M. 2022 · 2022
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Domain Adaptation via Prompt Learning
Ge, C.; Huang, R.; Xie, M.; Lai, Z.; Song, S.; Li, S.; and Huang, G. 2022 · 2022
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Test time adaptation via conjugate pseudo-labels
Goyal, S.; Sun, M.; Raghunathan, A.; and Kolter, J. Z. 2022 · 2022
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Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
Hoyer, L.; Dai, D.; and Van Gool, L. 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. 2022b · 2022
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Uncertainty-aware unsupervised domain adaptation in object detection
Guan, D.; Huang, J.; Xiao, A.; Lu, S.; and Cao, Y. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; and Neubig, G. 2021 · 2021
Cited alongside, same era.
Vision Transformers for Dense Prediction
Ranftl, R.; Bochkovskiy, A.; and Koltun, V. 2021 · 2021
Cited alongside, same era.
ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding
Sakaridis, C.; Dai, D.; and Van Gool, L. 2021 · 2021
Cited alongside, same era.
Dacs: Domain adaptation via cross-domain mixed sampling
Tranheden, W.; Olsson, V.; Pinto, J.; and Svensson, L. 2021 · 2021
Cited alongside, same era.
Tent: Fully Test-Time Adaptation by Entropy Minimization
Wang, D.; Shelhamer, E.; Liu, S.; Olshausen, B. A.; and Darrell, T. 2021 · 2021
Cited alongside, same era.
Liu, J.; Zhang, Q.; Li, J.; Lu, M.; Huang, T.; and Zhang, S. 2022 · 2022
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Real-World Robot Learning with Masked Visual Pre-training
Radosavovic, I.; Xiao, T.; James, S.; Abbeel, P.; Malik, J.; and Darrell, T. 2022 · 2022
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Uncertainty-guided source-free domain adaptation
Roy, S.; Trapp, M.; Pilzer, A.; Kannala, J.; Sebe, N.; Ricci, E.; and Solin, A. 2022 · 2022
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Fine-tuning Image Transformers using Learnable Memory
Sandler, M.; Zhmoginov, A.; Vladymyrov, M.; and Jackson, A. 2022 · 2022
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MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
Shin, I.; Tsai, Y.-H.; Zhuang, B.; Schulter, S.; Liu, B.; Garg, S.; Kweon, I. S.; and Yoon, K.-J. 2022 · 2022
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CD-TTA: Compound Domain Test-time Adaptation for Semantic Segmentation
Song, J.; Park, K.; Shin, I.; Woo, S.; and Kweon, I. S. 2022 · 2022
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In Search for a Generalizable Method for Source Free Domain Adaptation
Boudiaf, M.; Denton, T.; van Merriënboer, B.; Dumoulin, V.; and Triantafillou, E. 2023 · 2023
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Test-time Domain Adaptation for Monocular Depth Estimation
Li, Z.; Shi, S.; Schiele, B.; and Dai, D. 2023 · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
Liang, J.; He, R.; and Tan, T. 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; and Neubig, G. 2023 · 2023
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Towards stable test-time adaptation in dynamic wild world
Niu, S.; Wu, J.; Zhang, Y.; Wen, Z.; Chen, Y.; Zhao, P.; and Tan, M. 2023 · 2023
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EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
Song, J.; Lee, J.; Kweon, I. S.; and Choi, S. 2023 · 2023
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Robust test-time adaptation in dynamic scenarios
Yuan, L.; Xie, B.; and Li, S. 2023 · 2023
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