- Clst, Yonatan Bisk, Ari Holtzman, Jesse Thomason, Ja-740 cob, Y. Bengio, Joyce Chai, Angeliki Lapata, Jonathan Lazaridou, Alek-742 May, Nicolas sandr Nisnevich, P. PintoJoseph, Turian, Ting Chen, Simon Kornblith, Mohammad Norouzi, Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El, Faisal Kholy, Zhe Ahmed, Yu Gan, Cheng, Zihan Dai, Hanxiao Liu, Quoc V. Le, Jia Deng, Wei Dong, R. Socher, Li-Jia Li, K. Li, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Jesse Dodge, Maarten Sap, Ana Marasovic, Gabriel Agnew, Dirk Ilharco, Groeneveld Matt, Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Yu Liu, Jianfeng Wang, M. Gao, Zhou, Xiaoyi Dong, Jia Bao, Ting Zhang, Dongdong, Weiming Chen, Lu Zhang, Dong Yuan, Fang Chen, Da-Cheng Juan, Chuntian Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom, Andrew Duerig, T. Sujith, Ravi, Lukasz Kaiser, Aidan N. Gomez, Noam M. Shazeer, Niki Vaswani, Llion Parmar, Jones Jakob, Uszko-850, Alex Kendall, Yarin Gal, Roberto Cipolla, Salman H. Khan, Muzammal Naseer, Munawar Hayat, Waqas Zamir, Fahad Shahbaz, Khan, Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin John-867, K. Hata, Joshua Kravitz, Stephanie Chen, Mike Lewis, Yinhan Liu, Marjan Naman Goyal, Abdelrahman Ghazvininejad, Omer Mohamed, Levy, Luke Zettlemoyer, Bohan Li, Hao Zhou, Jun-Tao He, Mingxuan Wang, Liunian Harold, Mark Li, Da Yatskar, Yin, Cho-Jui, Kai-Wei Chang, Visualbert. 2022. S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning. Abstract: In this review, we aim to inspire research into 001 S elf-S upervised S hared S emantic S pace ( S5 ) 002 multimodal learning problems. We equip non-003 expert researchers with a framework of in-004 formed modeling decisions via an extensive 005 literature review, an actionable modeling check-006 list, as well as a series of novel zero-shot eval-007 uation tasks. The core idea for our S5 check-008 list lies in learning contextual multimodal in-009 teractions at various granularity levels via a 010 shared Transformer encoder with a denoising 011 loss term, which is also regularized by a con-012 trastive loss term to induce a semantic align-013 ment prior on the contextual embedding space. 014 Essentially, we aim to model human concept 015 understanding and thus learn to “put a name to 016 a face”. This ultimately enables interpretable 017 zero-shot S5 generalization on a variety of 018 novel downstream tasks. In summary, this re-019 view provides sufficient background and ac-020 tionable strategies for training cutting-edge S5 021 multimodal networks. 022
