Shaping Visual Representations with Language for Few-Shot Classification (2020.acl-main)
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| Challenge: | Existing models use natural language descriptions to classify images, but no model uses it for new tasks. |
| Approach: | They propose a model that regularizes visual representations to predict language in a few-shot setting . they propose to use language to improve few- shot visual classification . |
| Outcome: | The proposed model outperforms baseline models in two challenging few-shot domains. |
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Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li
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