arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2203.01922cs.CVcs.AIcs.CL
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视觉语言智能:任务、表示学习与大型模型

Vision-Language Intelligence: Tasks, Representation Learning, and Large Models

The Hong Kong University of Science and Technology(香港科技大学);Chinese Academy of Science(中国科学院);International Digital Economy Academy (IDEA)(国际数字经济研究院);The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州));Microsoft Research(微软研究院);Tsinghua University(清华大学)

Feng Li, Hao Zhang, Yi-Fan Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, PengChuan Zhang, Lei Zhang

摘要:

本文从时间的角度对视觉语言智能进行了全面的考察。这项调查的灵感来自计算机视觉和自然语言处理的显着进步,以及最近从单模态处理转向多模态理解的趋势。我们将这一领域的发展总结为三个时期,即特定于任务的方法,视觉语言预训练(VLP)方法和由大规模弱标记数据授权的更大模型。我们首先以一些常见的虚拟现实任务为例,介绍了特定于任务的方法的发展。然后,我们专注于VLP方法,全面回顾模型结构和训练方法的关键组成部分。在此之后,我们展示了最近的工作如何利用大规模的原始图像-文本数据来学习语言对齐的视觉表示,这些视觉表示在零或少样本学习任务中更好地泛化。最后,我们讨论了一些潜在的未来趋势,模态合作,统一表示,知识整合。我们相信,这篇综述将有助于人工智能和机器学习的研究人员和从业者,特别是那些对计算机视觉和自然语言处理感兴趣的人。

英文摘要:

This paper presents a comprehensive survey of vision-language (VL) intelligence from the perspective of time. This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension. We summarize the development in this field into three time periods, namely task-specific methods, vision-language pre-training (VLP) methods, and larger models empowered by large-scale weakly-labeled data. We first take some common VL tasks as examples to introduce the development of task-specific methods. Then we focus on VLP methods and comprehensively review key components of the model structures and training methods. After that, we show how recent work utilizes large-scale raw image-text data to learn language-aligned visual representations that generalize better on zero or few shot learning tasks. Finally, we discuss some potential future trends towards modality cooperation, unified representation, and knowledge incorporation. We believe that this review will be of help for researchers and practitioners of AI and ML, especially those interested in computer vision and natural language processing.

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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