Fetching the paper…
Reading the bibliography…
Multi-Task Learning (MTL) is designed to train multiple correlated tasks simultaneously, thereby enhancing the performance of individual tasks.
Learning multiple tasks with multilinear relationship networks
Long, M.; Cao, Z.; Wang, J.; and Yu, P. S. 2017 · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Venkateswara, H.; Eusebio, J.; Chakraborty, S.; and Panchanathan, S. 2017 · 2017
Earlier work this paper cites.
Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction
Gao, Y.; Ma, J.; Zhao, M.; Liu, W.; and Yuille, A. L. 2019 · 2019
Earlier work this paper cites.
Which tasks should be learned together in multi-task learning?
Standley, T.; Zamir, A.; Chen, D.; Guibas, L.; Malik, J.; and Savarese, S. 2020 · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2021 · 2021
Earlier work this paper cites.
Efficiently identifying task groupings for multi-task learning
Fifty, C.; Amid, E.; Zhao, Z.; Yu, T.; Anil, R.; and Finn, C. 2021 · 2021
Earlier work this paper cites.
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Gao, P.; Geng, S.; Zhang, R.; Ma, T.; Fang, R.; Zhang, Y.; Li, H.; and Qiao, Y. 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Variational multi-task learning with gumbel-softmax priors
Shen, J.; Zhen, X.; Worring, M.; and Shao, L. 2021 · 2021
Earlier work this paper cites.
Transfer vision patterns for multi-task pixel learning
Zhang, X.; Zhou, L.; Li, Y.; Cui, Z.; Xie, J.; and Yang, J. 2021 · 2021
Cited alongside, same era.
Domain adaptive ensemble learning
Zhou, K.; Yang, Y.; Qiao, Y.; and Xiang, T. 2021 · 2021
Cited alongside, same era.
Adaptformer: Adapting vision transformers for scalable visual recognition
Chen, S.; Ge, C.; Tong, Z.; Wang, J.; Song, Y.; Wang, J.; and Luo, P. 2022 · 2022
Cited alongside, same era.
Hyperprompt: Prompt-based task-conditioning of transformers
He, Y.; Zheng, S.; Tay, Y.; Gupta, J.; Du, Y.; Aribandi, V.; Zhao, Z.; Li, Y.; Chen, Z.; Metzler, D.; et al. 2022 · 2022
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2022 · 2022
Cited alongside, same era.
Grounded language-image pre-training
Li, L. H.; Zhang, P.; Zhang, H.; Yang, J.; Li, C.; Zhong, Y.; Wang, L.; Yuan, L.; Zhang, L.; Hwang, J.-N.; et al. 2022 · 2022
Groupvit: Semantic segmentation emerges from text supervision
Xu, J.; De Mello, S.; Liu, S.; Byeon, W.; Breuel, T.; Kautz, J.; and Wang, X. 2022 · 2022
Later among the works it cites.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E. B.; Goldberg, Y.; and Ravfogel, S. 2022 · 2022
Later among the works it cites.
Regionclip: Region-based language-image pretraining
Zhong, Y.; Yang, J.; Zhang, P.; Li, C.; Codella, N.; Li, L. H.; Zhou, L.; Dai, X.; Yuan, L.; Li, Y.; et al. 2022 · 2022
Later among the works it cites.
Learning to prompt for vision-language models
Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2022 · 2022
Later among the works it cites.
Maple: Multi-modal prompt learning
Khattak, M. U.; Rasheed, H. A.; Maaz, M.; Khan, S.; and Khan, F. S. 2023 · 2023
Closest in time.
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Liu, Y.-C.; Ma, C.-Y.; Tian, J.; He, Z.; and Kira, Z. 2022 · 2022
Cited alongside, same era.
Denseclip: Language-guided dense prediction with context-aware prompting
Rao, Y.; Zhao, W.; Chen, G.; Tang, Y.; Zhu, Z.; Huang, G.; Zhou, J.; and Lu, J. 2022 · 2022
Cited alongside, same era.
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
Sung, Y.; Cho, J.; and Bansal, M. 2022 · 2022
Cited alongside, same era.
Robust fine-tuning of zero-shot models
Wortsman, M.; Ilharco, G.; Kim, J. W.; Li, M.; Kornblith, S.; Roelofs, R.; Lopes, R. G.; Hajishirzi, H.; Farhadi, A.; Namkoong, H.; et al. 2022 · 2022
Cited alongside, same era.
Visual prompt tuning
Jia, M.; Tang, L.; Chen, B.; Cardie, C.; Belongie, S. J.; Hariharan, B.; and Lim, S. 2022a
Cited in the paper.
Visual Prompt Tuning
Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022b
Cited in the paper.
Liang, X.; Niu, M.; Han, J.; Xu, H.; Xu, C.; and Liang, X. 2023 · 2023
Closest in time.
Hierarchical Prompt Learning for Multi-Task Learning
Liu, Y.; Lu, Y.; Liu, H.; An, Y.; Xu, Z.; Yao, Z.; Zhang, B.; Xiong, Z.; and Gui, C. 2023 · 2023
Closest in time.
Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning
Wang, Q.; Du, J.; Yan, K.; and Ding, S. 2023 · 2023
Closest in time.
DeMT: Deformable mixer transformer for multi-task learning of dense prediction
Xu, Y.; Yang, Y.; and Zhang, L. 2023 · 2023
Closest in time.
Taskprompter: Spatial-channel multi-task prompting for dense scene understanding
Ye, H.; and Xu, D. 2023 · 2023
Closest in time.