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In-context segmentation has drawn increasing attention with the advent of vision foundation models.
The pascal visual object classes (voc) challenge
Everingham, M.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
A benchmark dataset and evaluation methodology for video object segmentation
Perazzi, F.; Pont-Tuset, J.; McWilliams, B.; Van Gool, L.; Gross, M.; and Sorkine-Hornung, A. 2016 · 2016
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One-Shot Learning for Semantic Segmentation
Shaban, A.; Bansal, S.; Liu, Z.; Essa, I.; and Boots, B. 2017 · 2017
Earlier work this paper cites.
Conditional Networks for Few-Shot Semantic Segmentation
Rakelly, K.; Shelhamer, E.; Darrell, T.; Efros, A. A.; and Levine, S. 2018 · 2018
Earlier work this paper cites.
Parameter-efficient transfer learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
Earlier work this paper cites.
Panet: Few-shot image semantic segmentation with prototype alignment
Wang, K.; Liew, J. H.; Zou, Y.; Zhou, D.; and Feng, J. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Earlier work this paper cites.
Prior guided feature enrichment network for few-shot segmentation
Tian, Z.; Zhao, H.; Shu, M.; Yang, Z.; Li, R.; and Jia, J. 2020 · 2020
Earlier work this paper cites.
Prototype mixture models for few-shot semantic segmentation
Yang, B.; Liu, C.; Li, B.; Jiao, J.; and Ye, Q. 2020 · 2020
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Segdiff: Image segmentation with diffusion probabilistic models
Amit, T.; Shaharbany, T.; Nachmani, E.; and Wolf, L. 2021 · 2021
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Few-shot segmentation without meta-learning: A good transductive inference is all you need?
Boudiaf, M.; Kervadec, H.; Masud, Z. I.; Piantanida, P.; Ben Ayed, I.; and Dolz, J. 2021 · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
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Parameter-efficient transfer learning with diff pruning
Guo, D.; Rush, A. M.; and Kim, Y. 2021 · 2021
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Classifier-free diffusion guidance
Ho, J.; and Salimans, T. 2021 · 2021
Earlier work this paper cites.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L.; and Liang, P. 2021 · 2021
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Simpler is better: Few-shot semantic segmentation with classifier weight transformer
Lu, Z.; He, S.; Zhu, X.; Zhang, L.; Song, Y.-Z.; and Xiang, T. 2021 · 2021
Earlier work this paper cites.
Diffusion probabilistic models for 3d point cloud generation
Luo, S.; and Hu, W. 2021 · 2021
Earlier work this paper cites.
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild
Miao, J.; Wei, Y.; Wu, Y.; Liang, C.; Li, G.; and Yang, Y. 2021 · 2021
Earlier work this paper cites.
Hypercorrelation squeeze for few-shot segmentation
Min, J.; Kang, D.; and Cho, M. 2021 · 2021
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2021 · 2021
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2021 · 2021
Earlier work this paper cites.
Few-shot semantic segmentation with cyclic memory network
Xie, G.-S.; Xiong, H.; Liu, J.; Yao, Y.; and Shao, L. 2021 · 2021
Earlier work this paper cites.
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B.; Donahue, J.; Luc, P.; Miech, A.; Barr, I.; Hasson, Y.; Lenc, K.; Mensch, A.; Millican, K.; Reynolds, M.; et al. 2022 · 2022
Cited alongside, same era.
Exploring visual prompts for adapting large-scale models
Bahng, H.; Jahanian, A.; Sankaranarayanan, S.; and Isola, P. 2022 · 2022
Cited alongside, same era.
Beit: Bert pre-training of image transformers
Bao, H.; Dong, L.; Piao, S.; and Wei, F. 2022 · 2022
Cited alongside, same era.
Visual prompting via image inpainting
Bar, A.; Gandelsman, Y.; Darrell, T.; Globerson, A.; and Efros, A. 2022 · 2022
Cited alongside, same era.
Label-efficient semantic segmentation with diffusion models
Baranchuk, D.; Rubachev, I.; Voynov, A.; Khrulkov, V.; and Babenko, A. 2022 · 2022
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Self-regularized prototypical network for few-shot semantic segmentation
Ding, H.; Zhang, H.; and Jiang, X. 2023 · 2023
Later among the works it cites.
Instructdiffusion: A generalist modeling interface for vision tasks
Geng, Z.; Yang, B.; Hang, T.; Li, C.; Gu, S.; Zhang, T.; Bao, J.; Zhang, Z.; Hu, H.; Chen, D.; et al. 2023 · 2023
Later among the works it cites.
Prototype adaption and projection for few-and zero-shot 3d point cloud semantic segmentation
He, S.; Jiang, X.; Jiang, W.; and Ding, H. 2023 · 2023
Later among the works it cites.
Prompt-to-prompt image editing with cross attention control
Hertz, A.; Mokady, R.; Tenenbaum, J.; Aberman, K.; Pritch, Y.; and Cohen-Or, D. 2023 · 2023
Later among the works it cites.
Khani, A.; Taghanaki, S. A.; Sanghi, A.; Amiri, A. M.; and Hamarneh, G. 2023 · 2023
Later among the works it cites.
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Gal, R.; Alaluf, Y.; Atzmon, Y.; Patashnik, O.; Bermano, A. H.; Chechik, G.; and Cohen-Or, D. 2022 · 2022
Cited alongside, same era.
Flexible diffusion modeling of long videos
Harvey, W.; Naderiparizi, S.; Masrani, V.; Weilbach, C.; and Wood, F. 2022 · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
Cited alongside, same era.
Cascaded diffusion models for high fidelity image generation
Ho, J.; Saharia, C.; Chan, W.; Fleet, D. J.; Norouzi, M.; and Salimans, T. 2022 · 2022
Cited alongside, same era.
Cost aggregation with 4d convolutional swin transformer for few-shot segmentation
Hong, S.; Cho, S.; Nam, J.; Lin, S.; and Kim, S. 2022 · 2022
Cited alongside, same era.
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. 2022 · 2022
Cited alongside, same era.
Learning what not to segment: A new perspective on few-shot segmentation
Lang, C.; Cheng, G.; Tu, B.; and Han, J. 2022 · 2022
Cited alongside, same era.
Few-shot biomedical image segmentation using diffusion models: Beyond image generation
Khosravi, B.; Rouzrokh, P.; Mickley, J. P.; Faghani, S.; Mulford, K.; Yang, L.; Larson, A. N.; Howe, B. M.; Erickson, B. J.; Taunton, M. J.; et al. 2023 · 2023
Later among the works it cites.
Universal few-shot learning of dense prediction tasks with visual token matching
Kim, D.; Kim, J.; Cho, S.; Luo, C.; and Hong, S. 2023 · 2023
Later among the works it cites.
Segment anything
Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W.-Y.; et al. 2023 · 2023
Later among the works it cites.
Le, M.-Q.; Nguyen, T. V.; Le, T.-N.; Do, T.-T.; Do, M. N.; and Tran, M.-T. 2023 · 2023
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GRES: Generalized Referring Expression Segmentation
Liu, C.; Ding, H.; and Jiang, X. 2023 · 2023
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Unified-io: A unified model for vision, language, and multi-modal tasks
Lu, J.; Clark, C.; Zellers, R.; Mottaghi, R.; and Kembhavi, A. 2023 · 2023
Later among the works it cites.
Harnessing Diffusion Models for Visual Perception with Meta Prompts
Wan, Q.; Huang, Z.; Kang, B.; Feng, J.; and Zhang, L. 2023 · 2023
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Mosaicfusion: Diffusion models as data augmenters for large vocabulary instance segmentation
Xie, J.; Li, W.; Li, X.; Liu, Z.; Ong, Y. S.; and Loy, C. C. 2023 · 2023
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Open-vocabulary panoptic segmentation with text-to-image diffusion models
Xu, J.; Liu, S.; Vahdat, A.; Byeon, W.; Wang, X.; and De Mello, S. 2023 · 2023
Later among the works it cites.
Diffusion probabilistic modeling for video generation
Yang, R.; Srivastava, P.; and Mandt, S. 2023 · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Zhang, L.; Rao, A.; and Agrawala, M. 2023 · 2023
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What Makes Good Examples for Visual In-Context Learning?
Zhang, Y.; Zhou, K.; and Liu, Z. 2023 · 2023
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Unleashing text-to-image diffusion models for visual perception
Zhao, W.; Rao, Y.; Liu, Z.; Liu, B.; Zhou, J.; and Lu, J. 2023 · 2023
Later among the works it cites.
DiffusionInst: Diffusion Model for Instance Segmentation
Gu, Z.; Chen, H.; Xu, Z.; Lan, J.; Meng, C.; and Wang, W. 2024 · 2024
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Transformer-Based Visual Segmentation: A Survey
Li, X.; Ding, H.; Zhang, W.; Yuan, H.; Cheng, G.; Jiangmiao, P.; Chen, K.; Liu, Z.; and Loy, C. C. 2024 · 2024
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Unigs: Unified representation for image generation and segmentation
Qi, L.; Yang, L.; Guo, W.; Xu, Y.; Du, B.; Jampani, V.; and Yang, M.-H. 2024 · 2024
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Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Sun, Z.; Fang, Y.; Wu, T.; Zhang, P.; Zang, Y.; Kong, S.; Xiong, Y.; Lin, D.; and Wang, J. 2024 · 2024
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Personalize segment anything model with one shot
Zhang, R.; Jiang, Z.; Guo, Z.; Yan, S.; Pan, J.; Dong, H.; Gao, P.; and Li, H. 2024 · 2024
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