arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2306.03421cs.CV
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多样化联合视觉-语言分词学习

Diversifying Joint Vision-Language Tokenization Learning

The Ohio State University(俄亥俄州立大学);Google DeepMind(谷歌DeepMind)

Vardaan Pahuja, AJ Piergiovanni, Anelia Angelova

AI总结:

针对视觉问答等任务的联合视觉-语言表示需兼具跨模态特征捕捉能力与多样性的问题,提出多样化分词学习的方法,学习解耦的跨模态token,在多数设置下优于基线且比肩SOTA。

摘要:

构建图像与文本的联合表示是视觉问答、视频问答等任务的关键步骤。本研究发现,这类表示不仅需要联合捕捉两种模态的特征,还应具备多样性以提升泛化性能。为此,我们提出通过丰富分词学习过程的多样性来开展联合视觉-语言表示学习,使得能够从两种模态中学习到彼此充分解耦的token。实验表明,我们的方法在多数设置下优于基线模型,且与当前最优方法性能相当。

英文摘要:

Building joint representations across images and text is an essential step for tasks such as Visual Question Answering and Video Question Answering. In this work, we find that the representations must not only jointly capture features from both modalities but should also be diverse for better generalization performance. To this end, we propose joint vision-language representation learning by diversifying the tokenization learning process, enabling tokens that are sufficiently disentangled from each other to be learned from both modalities. We observe that our approach outperforms the baseline models in a majority of settings and is competitive with state-of-the-art methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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