5月27日,Meta发布了Vision Language Model行业研究的综述论文《An Introduction to Vision-Language Modeling》。全文干货满满,但篇幅过长,所以笔者把每一章节放到独立的文章中,方便对VLM领域感兴趣的同学们一边读一边翻译一边消化,觉得有用就一键三连吧~ *本文只摘译精华部分,需要了解全文的请至文末跳转至原文链接阅读。
MaskedLM/PrefixLM:mask部分token,同时利用剩余的text和图像token预测被mask掉的token Masked Vision Modeling:mask掉图像的部分patch,然后预测,可以回归也可以分类 Vision-Language Matching:使用image和language的匹配任务作为预训练目标 Vision-Language Contrastive Learning:基于对比学习的图文任务 Word-Region Aligment:对齐...
1)经典的MLM任务,旨在预测给定输入中的缺失部分; 2)句子图像预测任务(NSP),旨在预测一个标题是否实际上描述了图像内容。 通过利用这两个目标,模型在多个视觉语言任务中表现出色,这主要是由于transformer模型通过注意力机制学习将单词与视觉线索关联起来的能力。 2.2 基于对比学习的VLMs发布...
根据经验,遵循 BERT 的 VLP 模型以 15% 的掩码率随机掩码每个文本输入 token,并在 80% 的时间使用特殊 token [MASK]、10% 的时间使用随机文本 token,剩余 10% 的时间使用原始 token 来替换被掩码掉的文本。不过在普林斯顿大学陈丹琦等人的论文《Should You Mask 15% in Masked Language Modeling?》中,作者发现...
MLM / ITM: Aligning parts of images with text with masked-language modeling and image-text matching objectives No Training: Using stand-alone vision and language models via iterative optimization Note that this section is a non-exhaustive list, and there are various other approaches, as well as...
Relation Priority 25.00 - - 9.19 29.19 26.49 31.35 25.00 - - 11.24 14.94 38.39 15.28 Mean 25.00 - - 21.07 35.13 36.19 23.36 [18], which introduces contextualized multimodal fusion with a co-attention mechanism and is additionally trained with masked languag...
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Faisal Ahmed, Zicheng Liu, Yumao Lu, Lijuan Wang ECCV|October 2022 Related File Download BibTex We propose UniTAB that Unifies Text And Bo...
Li, “Tip-adapter: Training-free clip-adapter for better vision-language modeling,” arXiv preprint arXiv:2111.03930, 2021. [42] J. Ding, N. Xue, G.-S. Xia, and D. Dai, “Decoupling zero-shot semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and ...
Different from the approach above using PVLMs, PromptHate[2] is a recently proposed model that converts the multimodal meme detection task into a unimodal masked language modeling task. It first generates meme image captions with an off-the-shelf image caption generator, ClipCap [25]. By conve...
综合训练目标:SyCoCa结合了图像-文本对比(Image-Text Contrasting, ITC)、图像字幕生成(Image Captioning, IC)和文本引导的遮蔽图像建模(Text-Guided Masked Image Modeling, TG-MIM)三个训练目标,以促进全局和局部的双向交互。 实验验证:论文在五个视觉-语言任务上进行了广泛的实验,包括图像-文本检索、图像字幕生成、...