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Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks.
Visualbert: A simple and performant baseline for vision and language
Li, L. H., Yatskar, M., Yin, D., Hsieh, C., and Chang, K · 1908
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Natural gradient works efficiently in learning
Amari, S.-I · 1998
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Ensemble methods in machine learning
Dietterich, T. G · 2000
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Microsoft coco captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L · 2015
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Generation and comprehension of unambiguous object descriptions
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A. L., and Murphy, K · 2016
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Modeling context in referring expressions
Yu, L., Poirson, P., Yang, S., Berg, A. C., and Berg, T. L · 2016
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When is multitask learning effective? semantic sequence prediction under varying data conditions
Alonso, H. M. and Plank, B · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Essentially no barriers in neural network energy landscape
Draxler, F., Veschgini, K., Salmhofer, M., and Hamprecht, F · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D. P., and Wilson, A. G · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
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Taskonomy: Disentangling task transfer learning
Zamir, A. R., Sax, A., Shen, W. B., Guibas, L. J., Malik, J., and Savarese, S · 2018
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Task2vec: Task embedding for meta-learning
Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C. C., Soatto, S., and Perona, P · 2019
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Uniter: Learning universal image-text representations
Chen, Y.-C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., and Liu, J · 2019
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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
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Visual entailment: A novel task for fine-grained image understanding
Xie, N., Lai, F., Doran, D., and Kadav, A · 2019
Cited alongside, same era.
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
Cited alongside, same era.
UNITER: universal image-text representation learning
Chen, Y., Li, L., Yu, L., Kholy, A. E., Ahmed, F., Gan, Z., Cheng, Y., and Liu, J · 2020
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M · 2020
Cited alongside, same era.
Large-scale adversarial training for vision-and-language representation learning
Gan, Z., Chen, Y.-C., Li, L., Zhu, C., Cheng, Y., and Liu, J · 2020
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
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Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., and Tang, J · 2021
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What to pre-train on? efficient intermediate task selection
Poth, C., Pfeiffer, J., Rücklé, A., and Gurevych, I · 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
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Lu, J., Goswami, V., Rohrbach, M., Parikh, D., and Lee, S · 2020
Cited alongside, same era.
What is being transferred in transfer learning?
Neyshabur, B., Sedghi, H., and Zhang, C · 2020
Cited alongside, same era.
Adapterfusion: Non-destructive task composition for transfer learning
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., and Gurevych, I · 2020
Cited alongside, same era.
Pruksachatkun, Y., Phang, J., Liu, H., Htut, P. M., Zhang, X., Pang, R. Y., Vania, C., Kann, K., and Bowman, S. R · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al · 2020
Cited alongside, same era.
It’s not just size that matters: Small language models are also few-shot learners
Schick, T. and Schütze, H · 2020
Cited alongside, same era.
Exploring and predicting transferability across nlp tasks
Vu, T., Wang, T., Munkhdalai, T., Sordoni, A., Trischler, A., Mattarella-Micke, A., Maji, S., and Iyyer, M · 2020
Cited alongside, same era.
Crossing the format boundary of text and boxes: Towards unified vision-language modeling
Yang, Z., Gan, Z., Wang, J., Hu, X., Ahmed, F., Liu, Z., Lu, Y., and Wang, L · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E. B., Ravfogel, S., and Goldberg, Y · 2021
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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 · 2022
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FLAVA: A foundational language and vision alignment model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., and Kiela, D · 2022
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Spot: Better frozen model adaptation through soft prompt transfer
Vu, T., Lester, B., Constant, N., Al-Rfou, R., and Cer, D · 2022
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Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework
Wang, P., Yang, A., Men, R., Lin, J., Bai, S., Li, Z., Ma, J., Zhou, C., Zhou, J., and Yang, H · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Prompt tuning for generative multimodal pretrained models
Yang, H., Lin, J., Yang, A., Wang, P., Zhou, C., and Yang, H · 2022
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Uni-perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks
Zhu, X., Zhu, J., Li, H., Wu, X., Li, H., Wang, X., and Dai, J · 2022
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Mou, C., Wang, X., Xie, L., Zhang, J., Qi, Z., Shan, Y., and Qie, X · 2023
Closest in time.
Identifying beneficial task relations for multi-task learning in deep neural networks
Bingel, J. and Søgaard, A · 2026
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