GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
并且,它将三个任务所需的text encoder和text decoder进行了合并,相同的结构层之间共享参数,比起ALBEF的模型结构简洁很多,模态交互也更加充分。 多模态模型(图像-文本)的普遍训练方法 代码:GitHub - salesforce/BLIP: PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language U...
项目地址:https://github.com/zhangy0822/USER(没发) 解析文章标题: USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval 基于动量对比的统一语义增强手段解决图像-文本检索问题 基于动量对比:动量对比学习是一个训练范式,整篇文章都在该框架。解决问题2 统一的语义增强:文本、图像的语...
Moreover, CGMM is much more efficient than state-of-the-art methods using interactive matching. The code is available at https://github.com/cyh-sj/CGMN. 展开 关键词: Image-text retrieval relation reasoning graph matching cross-modal matching 年份: 2022 ...
//x-decoder-vl.github.io. 1. Introduction Visual understanding at different levels of granularity has been a longstanding problem in the vision community. The tasks span from image-level tasks (e.g., image classification [15], image-text retrieval, image captioning [8], and visual question ...
The code of this paper is available at https://github.com/dylls/Unsupervised_Text-to-Image_Synthesis. Introduction Recently, synthesizing images from natural language descriptions [1], [2] has been attracting more and more attentions in research communities, due to its great importance in many ...
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GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
which is the first unified and dynamic multimodal interaction framework towards image-text retrieval. In particular, we first design four types of cells as basic units to explore different levels of modality interactions, and then connect them in a dense strategy to construct a routing space. To ...