Summary of Embracing Language Inclusivity and Diversity in Clip Through Continual Language Learning, by Bang Yang et al.
Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
by Bang Yang, Yong Dai, Xuxin Cheng, Yaowei Li, Asif Raza, Yuexian Zou
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary of the abstract is as follows: This paper proposes a novel approach to extend vision-language pre-trained models’ language capacity by continual language learning (CLL). The proposed model, CLL-CLIP, builds upon CLIP and introduces an expandable token embedding layer to handle linguistic differences. To alleviate catastrophic forgetting, the authors propose a novel approach that ensures identical distribution of token embeddings during initialization and regularizes token embedding learning during training. A CLL benchmark is constructed covering 36 languages based on MSCOCO and XM3600 datasets, and multilingual image-text retrieval performance is evaluated. The results show that the proposed approach can boost CLL-CLIP, for example, by 6.7% in text-to-image average Recall@1 on XM3600, and improve various state-of-the-art methods consistently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary of the abstract is as follows: This paper is about making language models that can understand many languages. These models are good at understanding some languages like English but not others. The authors want to make a model that can learn new languages without forgetting old ones. They propose a new way to do this and test it on 36 different languages. The results show that their method is better than other methods for remembering old languages while learning new ones. |
Keywords
» Artificial intelligence » Embedding » Recall » Token
